zero trust network design

Zero Trust SASE

Zero Trust SASE

The traditional network perimeter has become increasingly difficult to define. Users work from offices, homes, mobile devices, and third-party locations, while applications and data are distributed across private data centres, SaaS platforms, and public clouds. In this environment, security cannot depend solely on whether a connection originates from a trusted corporate network. Organizations need a way to evaluate access continuously while delivering security and connectivity closer to users and applications.

Zero Trust provides the security model for this approach. Its core principle is that access should not be automatically trusted simply because a user or device is inside a particular network. Authentication and authorization should consider factors such as user identity, device posture, application, location, risk, and the sensitivity of the requested resource. Access can then be granted according to policy and the level of access actually required.

SASE, or Secure Access Service Edge, addresses the architectural side of the problem. It brings networking and security capabilities together through a distributed, cloud-delivered model designed to support users and applications wherever they are located. Instead of forcing all traffic through a traditional central data centre, security and connectivity services can be positioned closer to users, branches, and cloud resources.

A SASE architecture can incorporate capabilities such as secure web gateways, cloud-delivered firewalls, Zero Trust Network Access, security controls, and data protection services. The exact combination varies between implementations and vendors, but the broader objective is consistent: provide secure, policy-driven access without depending on a fixed network perimeter.

The combination of Zero Trust principles with SASE can simplify how organizations approach distributed access. Identity, device context, application requirements, security policy, and network connectivity can be evaluated together rather than treated as completely separate functions. This can help reduce unnecessary access while providing users with more direct and consistent connectivity to the applications they need.

The model also changes how security teams think about visibility and control. Rather than focusing only on network boundaries, they need to understand who is accessing an application, from which device, under what conditions, and what activity occurs after access is granted. Monitoring and policy enforcement therefore become continuous parts of the architecture rather than one-time decisions at the network edge.

Zero Trust and SASE should not be viewed as a single product or a quick replacement for existing security infrastructure. They represent architectural approaches that require careful consideration of identity, applications, devices, connectivity, security controls, legacy systems, and operational processes. When these elements are designed together, Zero Trust and SASE can provide a more adaptable security architecture for organizations operating across distributed users, networks, and cloud environments.

Zero Trust SASE • Risk-Based Access

Zero Trust Access Decision

See how identity, device posture and context are evaluated before access is granted. Zero Trust does not automatically trust the user simply because they are on a known network.

ACCESS DECISION ENGINE
User Request Who is requesting access? Identity User • role • authentication • MFA Device Posture Healthy • managed • compliant Context Location • time • behaviour • resource Risk & Policy Engine Evaluate trust against policy ALLOW Trusted request MFA Verify again LIMITED Restrict access DENY Block request
LEARNING PANEL
Selected

Trusted User

A trusted request still passes through the Zero Trust decision process. Trust is evaluated rather than assumed.

IDENTITY Authenticated user with valid MFA
DEVICE Managed and compliant
CONTEXT Normal behaviour and expected resource
DECISION ALLOW — application access granted

Highlights: Zero Trust SASE

Innovative Security Framework

Zero Trust SASE is an innovative security framework that combines Zero Trust principles with Secure Access Service Edge (SASE) architecture. It emphasizes continuous verification and validation of every user, device, and network resource attempting to access an organization’s network, regardless of location. By adopting a zero-trust approach, organizations can enhance security by eliminating the assumption of trust and implementing stricter access controls.

1. Note: Zero Trust SASE is built upon several key components to create a robust and comprehensive security framework. These components include identity and access management, multi-factor authentication, network segmentation, encryption, continuous monitoring, and threat intelligence integration. Each element is crucial in strengthening network security and protecting against evolving cyber threats.

2. Note: Both SASE and ZTNA are essential components of modern security architecture. However, they are two different solutions. SASE provides a comprehensive, multi-faceted security framework, while ZTNA is a more narrowly focused model focused on limiting resource access, which is a part of SAS

SASE + Zero Trust — Unified Secure Access for Modern Networks

SASE (Secure Access Service Edge) and Zero Trust form a unified architecture for securing modern distributed environments. SASE delivers networking and security controls through a cloud‑based framework, while Zero Trust defines how access should be verified, limited, and continuously monitored. Together, they replace legacy perimeter‑based models with identity‑driven, context‑aware access that follows users, devices, and applications wherever they operate. SASE provides the delivery mechanism—SD‑WAN, SWG, CASB, FWaaS, ZTNA—while Zero Trust provides the decision engine, enforcing least‑privilege access and continuous verification.

— Identity as the Foundation of Secure Access

Zero Trust requires explicit verification of every user, device, and session. Identity becomes the primary control point, replacing network location as the basis for trust. SASE operationalizes this by applying identity‑aware policies at distributed cloud enforcement points rather than centralized data centers. This ensures consistent protection across remote workers, branch sites, and cloud workloads. Identity signals—user role, device posture, authentication strength, and behavioral context—drive access decisions, aligning with NIST Zero Trust principles.

— Continuous Verification and Context‑Aware Access

Zero Trust eliminates implicit trust by continuously evaluating risk throughout the session. SASE enforces this through ZTNA, SWG, CASB, and FWaaS delivered at the cloud edge. Every request is assessed based on identity, device health, location, application sensitivity, and behavioral anomalies. If risk increases—unusual commands, suspicious cloud activity, degraded device posture—policies tighten automatically. This adaptive enforcement ensures that access is always aligned with real‑time context rather than static rules.

— Replacing VPNs with Application‑Level Access

Zero Trust SASE replaces traditional VPNs with ZTNA, granting access only to specific applications rather than entire network segments. This eliminates over‑privileged access and reduces lateral movement risk. Users connect directly to the application they are authorized to use, not the network. This model is especially critical as hybrid work expands and VPN concentrators become bottlenecks and security liabilities. ZTNA inside SASE becomes the modern remote‑access architecture.

— Cloud‑Delivered Security at the Edge

SASE shifts security from centralized appliances to distributed cloud points of presence (PoPs). SWG, CASB, DNS security, FWaaS, and ZTNA are applied close to users and applications, improving performance and reducing backhaul. This cloud‑delivered model supports hybrid work, SaaS adoption, and multi‑cloud architectures by enforcing consistent policies across all edges—branch, remote, and cloud. Zero Trust ensures that these policies are identity‑driven and continuously validated.

— Zero Trust SASE — One Policy Engine for All Traffic

Zero Trust SASE unifies private‑app access, web traffic inspection, SaaS governance, and WAN security under a single policy engine. Identity, device posture, data sensitivity, and application behavior feed into one decision framework. This ensures that least‑privilege access applies not only to private applications but also to SaaS, AI traffic, encrypted channels, and cloud workloads. Modern Zero Trust SASE platforms integrate AI visibility, DLP, and app control to secure data paths—not just ports and protocols.

**Challenge: The Lag in Security** 

Today’s digital transformation and strategy initiatives require speed and agility in I.T. However, there is a lag, and that lag is with security. Security can either hold them back or not align with the fluidity needed for agility. As a result, we have decreased an organization’s security posture, which poses a risk that needs to be managed. We have a lot to deal with, such as the rise in phishing attacks, mobile malware, fake public Wi-Fi networks, malicious apps, and data leaks. Therefore, we have new requirements that SASE can help with.

Zero Trust Security

Zero Trust Security is a paradigm shift from the traditional perimeter-based security model. It operates on the principle of “never trust, always verify.” Unlike the old approach, where users and devices were granted broad access once inside the network, Zero Trust Security treats every user, device, and network segment as potentially untrusted. This enhanced approach minimizes the risk of unauthorized access and lateral movement within the network.

Continuous Verification & Strict Access Control

Zero Trust is a security model that operates on the principle of never trusting any network or user by default. It emphasizes continuous verification and strict access control to mitigate potential threats. With Zero Trust, organizations adopt a granular approach to security, ensuring that every user, device, and application is authenticated and authorized before accessing any resources.

Zero Trust SASE Through Unified Access & Steering

This lab demonstrates how Zero Trust SASE evaluates every access request using identity, posture, and behavioural signals before allowing traffic to move across the network. By analysing trustScore, anomalyScore, burst‑traffic behaviour, device posture, and identity tokens, the Zero Trust SASE engine builds a complete security context for each session. This lightweight model shows learners how Zero Trust SASE continuously validates both the user and the device before any routing or access decision is made.

Unified Access and Steering

When the Zero Trust SASE server receives a request, it calculates a risk score that reflects identity strength, posture health, and behavioural stability. This risk score drives the enforcement outcome, shifting the session between allow, MFA‑required, limited‑access, or deny. Because the test payload represents a trusted and stable session, the Zero Trust SASE engine returns an allow decision. This makes it easy for learners to see how Zero Trust SASE uses risk to adapt its security posture in real time.Zero Trust Engine

The Zero Trust SASE lab provides full visibility into how access decisions are made. The server prints the payload, headers, risk breakdown, and final enforcement action, while the client displays the JSON decision returned by the engine. This end‑to‑end flow illustrates how Zero Trust SASE merges identity validation, posture assessment, and behavioural analytics into a single enforcement pipeline. With this foundation in place, learners can expand into more advanced Zero Trust SASE topics such as microsegmentation, continuous authentication, and dynamic session re‑evaluation.
Zero Trust Enforcement Engine

Zero Trust SASE Through Behaviour‑Driven Trust

This lab demonstrates how Zero Trust SASE strengthens security by evaluating every client request using behavioural signals rather than relying solely on identity or tokens. By analysing request frequency, metadata consistency, and error patterns, the system builds a dynamic trust profile for each client. This ensures that Zero Trust SASE policies continuously validate every action, preventing risky behaviour from moving across the edge‑to‑cloud environment.

The risk engine applies weighted rules to burst‑traffic spikes, identity mismatches, repeated failures, and trust‑score influence to generate a single risk value. This multi‑factor scoring model reflects how Zero Trust SASE depends on combining diverse indicators to detect anomalies across distributed branches, cloud edges, and secure gateways. As risk increases, the system escalates its security posture, shifting from normal to monitored to quarantine service to protect the SASE fabric.

Once the risk score is computed, the steering engine selects the appropriate response path. Low‑risk behaviour is routed normally, medium‑risk behaviour is monitored, and high‑risk behaviour is quarantined. This adaptive routing illustrates how Zero Trust SASE enforces dynamic segmentation, ensuring that suspicious behaviour triggers stronger controls while legitimate application flows continue securely across the network.

Zero Trust SASE Through Identity‑Centric Access Control

This server file demonstrates how Zero Trust SASE begins with strict identity validation before any traffic is allowed to reach protected services. The login endpoint issues JWT tokens that act as authenticated identity credentials, mirroring how SASE platforms verify users before granting access to cloud or edge resources. The JWT middleware enforces continuous authentication, ensuring that every request is checked rather than relying on any implicit trust. By anchoring all routing decisions to verified identity, the application reflects the Zero Trust SASE principle that access must always be authenticated, authorized, and continuously validated

SD WAN Policy

Once identity is confirmed, the application applies Zero Trust SASE logic by evaluating multiple contextual attributes—user role, application version, and client IP—to determine how traffic should be routed. This mirrors how SASE engines combine identity, device posture, application metadata, and network origin to enforce granular access policies. The policy engine dynamically selects the correct backend service based on these combined factors, demonstrating how Zero Trust SASE uses layered, context‑driven checks to classify flows and apply precise routing decisions across distributed cloud environments. Every request is steered according to explicit policy rather than network location or assumed trust.SDN WAN Node Js

The overall behavior of this file showcases how Zero Trust SASE can be implemented through dynamic segmentation and secure path selection. After a request passes identity validation and policy evaluation, the application determines the correct backend service, effectively creating isolated service paths based on role, version, and network origin. This reflects SASE architectures where segmentation is enforced through identity‑driven policy rather than static network boundaries, ensuring that each flow reaches only the services it is authorized to access. By combining identity, metadata, and IP context, the application demonstrates how Zero Trust SASE delivers adaptive, policy‑driven control over traffic movement across cloud and edge environments.
SD WAN security testing
SD WAN Security testing

Knowledge Checks: Microsegmentation

**Understanding Microsegmentation**

Microsegmentation is a network security technique that involves creating secure zones within a data center, enabling organizations to isolate different workloads from each other. This isolation limits the potential for lateral movement by attackers, thereby reducing the attack surface. Unlike traditional network segmentation, which often relies on physical barriers, microsegmentation uses software-defined policies to manage and secure traffic between segments.

**Benefits of Microsegmentation**

The primary advantage of microsegmentation is its ability to provide granular security controls. By implementing policies at the workload level, organizations can tailor security measures to specific applications and data. This not only enhances protection but also improves compliance with regulatory standards. Additionally, microsegmentation offers greater visibility into network traffic, allowing for more effective monitoring and threat detection.

Challenge: Large Segments with VLANs

Example Technology: Network Endpoint Groups

#### What Are Network Endpoint Groups?

Network Endpoint Groups are collections of endpoints, which can be instances, IP addresses, or other network entities, that share the same configuration within Google Cloud. NEGs are pivotal for managing traffic and balancing loads across multiple endpoints, ensuring that applications remain available and performant even under heavy demand. This flexibility allows businesses to seamlessly scale their operations without compromising on efficiency or security.

#### Types of Network Endpoint Groups

Google Cloud provides different types of NEGs to cater to various networking needs:

1. **Zonal NEGs**: These are used for backend services within a single zone, offering reliability and low-latency connections.

2. **Internet NEGs**: Designed for external services, these endpoints can reside outside of Google Cloud, enabling effective hybrid cloud architectures.

3. **Serverless NEGs**: Perfect for serverless applications, these allow you to integrate Cloud Run, App Engine, and Cloud Functions with your load balancer.

Each type serves distinct use cases, making it vital to choose the right NEG for your specific application requirements.

**Understanding Micro-segmentation**

Microsegmentation is a critical strategy in modern network management, providing a method to improve security by dividing a network into smaller, isolated segments. This approach ensures that any potential security breaches are contained and do not spread across the network. In the context of Google Cloud, NEGs can be effectively used to implement microsegmentation. By creating smaller, controlled segments, you can enforce security policies more rigorously, reducing the risk of unauthorized access and enhancing the overall security posture of your applications.


network endpoint groups

**The SASE Concept**

Gartner coined the SASE concept after seeing a pattern emerge in cloud and SD-WAN projects where full security integration was needed. We now refer to SASE as a framework and a security best practice. SASE leverages multiple security services into a framework approach.

The idea of SASE was not far from what we already did, which was integrating numerous security solutions into a stack that ensured a comprehensive, layered, secure access solution. By calling it a SASE framework, the approach to a complete solution somehow felt more focused than what the industry recognized as a best security practice.

The security infrastructure and decisions must become continuous and adaptive, not static, that formed the basis of traditional security methods. Consequently, we must enable real-time decisions that balance risk, trust, and opportunity. As a result, security has beyond a simple access control list (ACL) and zone-based segmentation based on VLANs. In reality, no network point acts as an anchor for security.

Example Technology: IPv6 Access Lists 

Many current network security designs and technologies were not designed to handle all the traffic and security threats we face today. This has forced many to adopt multiple-point products to address the different requirements. Remember that for every point product, there is an architecture to deploy, a set of policies to configure, and a bunch of logs to analyze. I find correlating logs across multiple-point product solutions used in different domains hard.

For example, a diverse team may operate the secure web gateways (SWG) to that of the virtual private network (VPN) appliances. It could be the case that these teams work in silos and are in different locations.

Zero Trust SASE requirements:

  1. Information hiding: SASE requires clients to be authenticated and authorized before accessing protected assets, regardless of whether the connection is inside or outside the network perimeter.
  2. Mutually encrypted connections: SASE uses the full TLS standard to provide mutual, two-way cryptographic authentication. Mutual TLS provides this and goes one step further to authenticate the client.
  3. Need to know the access model: SASE employs a need-to-know access model. As a result, SASE permits the requesting client to view only the resources appropriate to the assigned policy.
  4. Dynamic access control: SASE deploys a dynamic firewall that starts with one rule – deny all. Then, requested communication is dynamically inserted into the firewall, providing an active firewall security policy instead of static configurations.
  5. Identity-driven access control: SASE provides adaptive, identity-aware, precision access for those seeking more precise access and session control to applications on-premises and in the cloud.

Starting Zero Trust

Endpoint Security 

Understanding ARP (Address Resolution Protocol)

ARP is a vital network communication protocol that maps an IP address to a physical MAC address. By maintaining an ARP table, endpoints can efficiently communicate within a network. 

Routes and gateways act as the pathways for data transmission between networks. Safeguarding these routes is crucial to ensure network integrity. We will discuss the significance of secure routing protocols, such as OSPF and BGP, and how they contribute to endpoint security. 

Netstat, short for Network Statistics, is a powerful command-line tool providing detailed information about network connections and statistics. This section will highlight the importance of using netstat for monitoring endpoint security. From identifying active connections to detecting suspicious activities, netstat empowers administrators to protect their networks proactively.

Understanding SELinux

SELinux is a robust security framework built into the Linux kernel. It provides fine-grained access control policies and mandatory access controls (MAC) to enforce system-wide security policies. Unlike traditional Linux discretionary access controls (DAC), SELinux operates on the principle of least privilege, ensuring that only authorized actions are allowed.

Organizations can establish a robust security posture for their endpoints by combining SELinux with zero trust principles. SELinux provides granular control over system resources, enabling administrators to define strict policies based on user roles, processes, and system components. This ensures that even if an endpoint is compromised, the attacker’s lateral movement and potential damage are significantly limited.

### Understanding Authentication in Vault

Authentication is the process of verifying the identity of a user or system. In Vault, this is achieved through various authentication methods such as tokens, AppRole, LDAP, GitHub, and more. Each method serves different use cases, allowing flexibility and scalability in managing access. Vault ensures that only authenticated users can access sensitive data, thus mitigating the risk of unauthorized access.

—

### The Role of Authorization

While authentication verifies identity, authorization determines what authenticated users can do. Vault uses policies to define the actions that users and applications can perform. These policies are written in HashiCorp Configuration Language (HCL) or JSON, and they provide a fine-grained control over access to secrets. By segregating duties and defining clear access levels, Vault helps prevent privilege escalation and minimizes the risk of data exposure.

—

### Managing Identity with Vault

Vault’s identity management capabilities allow organizations to unify identities across various platforms. By integrating with identity providers and managing roles and entities, Vault simplifies user management and enhances security. This integration ensures that user credentials are consistently verified and that access rights are updated as roles change, reducing the risk of stale credentials being exploited.

—

Vault

Use Case: WAN Edge Performance Routing

SASE & Performance-Based Routing

Performance-based routing is a dynamic routing technique that selects the best path for network traffic based on real-time performance metrics. Traditional routing protocols often follow static routes, leading to suboptimal network performance. However, performance-based routing leverages latency, packet loss, and bandwidth availability metrics to make informed routing decisions. By continuously evaluating these metrics, networks can adapt and reroute traffic to ensure optimal performance.

Google Cloud & IAP

**Understanding the Basics of IAP**

At its core, Identity-Aware Proxy is a security service that acts as a gatekeeper for applications and resources. It ensures that only authenticated and authorized users can access specific web applications hosted on cloud platforms. Unlike traditional security models that rely on network-level access controls, IAP takes a user-centric approach, verifying identity and context before granting access. This method not only strengthens security but also simplifies access management across distributed environments.

**The Role of IAP in Google Cloud**

Google Cloud offers a versatile and integrated approach to using IAP, making it an attractive option for organizations leveraging cloud services. With Google Cloud’s IAP, businesses can secure their web applications and VMs without the need for traditional VPNs or complex network configurations. This section will delve into how Google Cloud implements IAP, highlighting its seamless integration with other Google Cloud services and the ease with which it can be deployed. By utilizing Google Cloud’s IAP, businesses can streamline their security operations and focus on delivering value to their customers.

**Benefits of Using Identity-Aware Proxy**

The advantages of implementing IAP are manifold. Firstly, it enhances security by enforcing granular access controls based on user identity and context. This reduces the risk of unauthorized access and potential data breaches. Secondly, IAP simplifies the user experience by enabling single sign-on (SSO) capabilities, allowing users to access multiple applications with a single set of credentials. Additionally, IAP’s integration with existing identity providers ensures that businesses can maintain a consistent security policy across their entire IT ecosystem.

Identity aware proxy

 

Zero Trust SASE • Least Privilege

Zero Trust Micro-Perimeter Lab

Compare broad network access with application-level access. ZTNA creates a smaller security boundary around the application instead of placing the user directly onto the network.

APPLICATION-LEVEL ACCESS
User Identity + device ZTNA Identity + Context Least Privilege Application Policy Application A Approved application ACCESS ALLOWED Application B Policy permitted ACCESS ALLOWED Database Higher privilege ACCESS BLOCKED
LEARNING PANEL
Selected

ZTNA Model

The user is not placed directly onto the network. Access is granted to specific applications based on identity and policy.

ACCESS MODEL Application-level access
TRUST BOUNDARY Micro-perimeter around the application
SECURITY RESULT Reduced lateral movement

Zero Trust SASE

Many challenges to existing networks and infrastructure create big security holes and decrease security posture. In reality, several I.T. components give the entity more access than required. We have considerable security flaws with using I.P. addresses as a security anchor and static locations; the virtual private networks (VPN) and demilitarized zone (DMZ) architectures used to establish access are often configured to allow excessive implicit trust.  

##Challenge 1: The issue with a DMZ

The DMZ is the neutral network between the Internet and your organization’s private network. It’s protected by a front-end firewall that limits Internet traffic to specific systems within its zone. The DMZ can have a significant impact on security if not appropriately protected. Remote access technologies such as VPN or RDP, often located in the DMZ, have become common targets of cyberattacks. One of the main issues I see with the DMZ is that the bad actors know it’s there. It may be secured, but it’s visible.

##Challenge 2: The issue with the VPN

In basic terms, a VPN provides an encrypted server and hides your IP address. However, the VPN does not secure users when they land on a network segment and is based on coarse-grained access control where the user has access to entire network segments and subnets. Traditionally, once you are on a segment, there will be no intra-filtering on that segment. That means all users in that segment need the same security level and access to the same systems, but that is not always the case. 

GRE without IPsec GRE with IPsec

##Challenge 3: permissive network access

VPNs generally provide broad, overly permissive network access with only fundamental access control limits based on subnet ranges. So, the traditional VPN provides overly permissive access and security based on I.P. subnets. Note: The issue with VLAN-based segmentation is large broadcast domains with free-for-all access. This represents a larger attack surface where lateral movements can take place. Below is a standard VLAN-based network running Spanning Tree Protocol ( STP ).

## Challenge 4: Security-based on trust

Much of the non-zero trust security architecture is based on trust, which bad actors abuse. On the other hand, examining a SASE overview includes zero trust networking and remote access as one of its components, which can adaptively offer the appropriate trust required at the time and nothing more. It is like providing a narrow segmentation based on many contextual parameters continuously assessed for risk to ensure the users are who they are and that the entities, either internal or external to the network, are doing what they are supposed to do.

**Removes excessive trust**

A core feature of SASE and Zero Trust is that it removes the excessive trust once required to allow entities to connect and collaborate. Within a zero-trust environment, our implicit trust in traditional networks is replaced with explicit identity-based trust with a default denial. With an identity-based trust solution, we are not just looking at IP addresses to determine trust levels. After all, they are just binary, deemed a secure private or a less trustworthy public. This assumption is where all of our problems started. They are just ones and zeros.

## Challenge 5: IP for Location and Identity 

To improve your security posture, it would be best to stop relying primarily on IP addresses and network locations as a proxy for trust. We have been doing this for decades. There is minimal context in placing a policy with legacy constructs. To determine the trust of a requesting party, we need to examine multiple contextual aspects, not just IP addresses.

And the contextual aspects are continuously assessed for security posture. This is a much better way to manage risk and allows you to look at the entire picture before deciding to enter the network or access a resource.

Example: Firewall Tagging

Firewall tags

1) SASE: First attempt to 

Organizations have adopted different security technologies to combat these changes and include them in their security stack. Many of the security technologies are cloud-based services. Some of these services include the cloud-based secure web gateway (SWG), content delivery network [CDN], and web application firewall [WAF].

A secure web gateway (SWG) protects users from web-based threats and applies and enforces acceptable corporate use policies. A content delivery network (CDN) is a geographically distributed group of servers that works together to deliver Internet content quickly. A WAF, or web application firewall, helps protect web applications by filtering and monitoring HTTP traffic between them and the Internet.

The data center is the center of the universe.

However, even with these welcomed additions to security, the general trend was that the data center is still the center of most enterprise networks and network security architectures. Let’s face it: These designs are becoming ineffective and cumbersome with the rise of cloud and mobile technology. Traffic patterns have changed considerably, and so has the application logic.

2) SASE: Second attempt to

The next attempt was for a converged cloud-delivered secure access service edge (SASE) to accomplish this shift in the landscape. And that is what SASE architecture does. As you know, the SASE architecture relies on multiple contextual aspects to establish and adapt trust for application-level access. It does not concern itself with significant VLANs and broad-level access or believe that the data center is the center of the universe. Instead, the SASE architecture is often based on PoPs, where each PoP acts as the center of the universe.

The SASE definition and its components are a transformational architecture that can combat many of these discussed challenges. A SASE solution converges networking and security services into one unified, cloud-delivered solution that includes the following core capabilities of sase.

From the network side of things: SASE in networking:

    1. Software-defined wide area network (SD-WAN)
    2. Virtual private network (VPN)
    3. Zero Trust Network ZTN
    4. Quality of service (QoS)
    5. Software-defined perimeter (SDP)

Example SDP Technology: VPC Service Controls

**What are VPC Service Controls?**

VPC Service Controls are a security feature offered by Google Cloud that allows organizations to define a security perimeter around their cloud resources. This perimeter helps prevent unauthorized access to sensitive data and provides an additional layer of protection against potential threats. By using VPC Service Controls, you can restrict access to your resources based on specific criteria, ensuring that only trusted entities can interact with your data.

**Key Benefits of Implementing VPC Service Controls**

Implementing VPC Service Controls offers several key benefits for organizations seeking to enhance their cloud security:

1. **Enhanced Data Security**: By creating a security perimeter around your cloud resources, you can reduce the risk of data breaches and ensure that sensitive information remains protected.

2. **Granular Access Control**: VPC Service Controls allow you to define access policies based on various factors such as source IP addresses, user identities, and more. This granular control ensures that only authorized users can access your resources.

3. **Simplified Compliance**: For organizations operating in regulated industries, compliance with data protection laws is critical. VPC Service Controls simplify the process of meeting regulatory requirements by providing a robust security framework.

4. **Seamless Integration**: Google Cloud’s VPC Service Controls integrate seamlessly with other Google Cloud services, allowing you to maintain a consistent security posture across your entire cloud environment.

**Setting Up VPC Service Controls**

Getting started with VPC Service Controls is a straightforward process. First, identify the resources you want to protect and define the security perimeter around them. Next, configure access policies to control who can access these resources and under what conditions. Google Cloud provides detailed documentation and tools to guide you through the setup process, ensuring a smooth implementation.

**Best Practices for Using VPC Service Controls**

To maximize the effectiveness of your VPC Service Controls, consider the following best practices:

– **Regularly Review Access Policies**: Periodically review and update access policies to ensure they align with your organization’s security requirements and industry standards.

– **Monitor and Audit Activity**: Use Google Cloud’s monitoring and logging tools to track access to your resources and identify any potential security incidents.

– **Educate Your Team**: Ensure that your team is well-versed in the use of VPC Service Controls and understands the importance of maintaining a secure cloud environment.

VPC Security Controls VPC Service Controls

From the security side of things, SASE capabilities in security:

    1. Firewall as a service (FWaaS)
    2. Domain Name System (DNS) security
    3. Threat prevention
    4. Secure web gateways
    5. Data loss prevention (DLP)
    6. Cloud access security broker (CASB)

Example Technology: The Web Security Scanner

### How Google Cloud’s Web Security Scanner Works

Google Cloud’s Web Security Scanner is a robust solution that integrates seamlessly with the Google Cloud environment. It automatically scans your web applications for common vulnerabilities, such as cross-site scripting (XSS), mixed content, and outdated libraries. The scanner’s intuitive interface provides detailed reports, highlighting potential issues and offering actionable recommendations for mitigation. This automation not only saves time but also ensures that your applications remain secure as you continue to develop and deploy new features.

### Key Features and Benefits

One of the standout features of Google Cloud’s Web Security Scanner is its ability to perform authenticated scans. This means it can test parts of your web application that require user login, ensuring a comprehensive security assessment. Additionally, the scanner is designed to work seamlessly with other Google Cloud services, making it a convenient choice for those already invested in the Google ecosystem. Its cloud-native architecture ensures that it scales efficiently to meet the needs of businesses, big and small.

### Best Practices for Using Web Security Scanners

To get the most out of your web security scanner, it’s important to integrate it into your continuous integration and deployment processes. Regularly scanning your applications ensures that any new vulnerabilities are promptly identified and addressed. Additionally, consider using the scanner alongside other security tools to create a multi-layered defense strategy. Training your development team on common security pitfalls can also help prevent vulnerabilities from being introduced in the first place.

security web scanner

SASE changes the focal point to the identity of the user and device. With traditional network design, we have the on-premises data center, considered the universe’s center. With SASE, that architecture changes this to match today’s environment and moves the perimeter to the actual user, devices, or PoP with some SASE designs. In contrast to traditional enterprise networks and security architectures, the internal data center is the focal point for access. 

Example Product: Cisco Meraki

### What is Cisco Meraki?

Cisco Meraki is a suite of cloud-managed IT solutions that include wireless, switching, security, EMM (Enterprise Mobility Management), and security cameras, all centrally managed from the web. The Meraki dashboard provides powerful and intuitive tools to manage your entire network from a single pane of glass. This holistic approach ensures that businesses can maintain robust security protocols without compromising on ease of management.

### Key Features of Cisco Meraki

#### Cloud-Based Management

One of the standout features of Cisco Meraki is its cloud-based management. This allows for real-time monitoring, configuration, and troubleshooting from anywhere in the world. With automatic updates and seamless scalability, businesses can ensure their network is always up-to-date and secure.

#### Advanced Security Features

Cisco Meraki offers a range of advanced security features designed to protect your network from various threats. These include intrusion detection and prevention systems (IDS/IPS), advanced malware protection (AMP), and content filtering. By leveraging these tools, businesses can safeguard their data and maintain the integrity of their network.

#### Simplified Deployment

Deploying a traditional network can be a complex and time-consuming task. Cisco Meraki simplifies this process with zero-touch provisioning, which allows devices to be pre-configured and managed remotely. This reduces the need for on-site technical expertise and accelerates the deployment process.

### Benefits of Using Cisco Meraki for Network Security

#### Centralized Control

The centralized control offered by the Meraki dashboard enables IT teams to manage multiple sites from a single interface. This not only streamlines operations but also ensures consistent security policies across all locations.

#### Scalability

As businesses grow, their network needs evolve. Cisco Meraki’s scalable solutions allow for easy expansion without the need for significant infrastructure changes. This flexibility ensures that businesses can adapt to changing demands without compromising on security.

#### Cost Efficiency

By reducing the need for on-site hardware and simplifying management, Cisco Meraki can lead to significant cost savings. Additionally, the reduced need for technical expertise can lower operational costs, making it an attractive option for businesses looking to optimize their IT budget.

VPN Security Scenario 

  • Challenge: Traditional remote access VPNs

Remote access VPNs are primarily built to allow users outside the perimeter firewall to access resources inside the perimeter firewall. As a result, they often follow a hub-and-spoke architecture, with users connected by tunnels of various lengths depending on their distance from the data center. Traditional VPNs introduce a lot of complexity. For example, what do you do if you have multiple sites where users need to access applications? In this scenario, the cost of management would be high. 

  • Challenge: Tunnel based on I.P

What’s happening here is that the tunnel creates an extension between the client device and the application location. The tunnel is based on IP addresses on the client device and the remote application. Now that there is I.P. connectivity between the client and the application, the network where the application is located is extended to the client.

However, the client might not sit in an insecure hotel room or from home. These may not be sufficiently protected, and such locations should be considered insecure. The traditional VPN has many issues to deal with. It is user-initiated, and policy often permits split-tunnel VPNs without Internet or cloud traffic inspection.

SASE: A zero-trust VPN solution

A SASE solution encompasses VPN services and enhances the capabilities of operating in cloud-based infrastructure to route traffic. On the other hand, with SASE, the client connects to the SASE PoP, which carries out security checks and forwards the request to the application. A SASE design still allows clients to access the application, but they can only access that specific application and nothing more, like a stripped-down VLAN known as a micro-segmentation.

Restricting Lateral Movements

Clients must pass security controls, and no broad-level access is susceptible to lateral movements. Access control is based on an allowlist rather than the traditional blocklist rule. Also, other variables present in the request context are used instead of using I.P. addresses as the client identifier. As a result, the application is now the access path, not the network.

Simplified Management & Policy Control

So, no matter what type of VPN services you use, the SASE provides a unified cloud to connect to instead of backhauling to a VPN gateway—simplifying management and policy control. Well-established technologies such as VPN, secure web gateway, and firewall are being reviewed and reassessed in Zero Trust remote access solutions as organizations revisit approaches that have been in place for over a decade. 

A recommendation: SASE and SD-WAN

The value of SD-WAN is high. However, it also brings many challenges, including new security risks. In some of my consultancies, I have seen unreliable performance and increased complexity due to the need for multiple overlays. Also, these overlays need to terminate somewhere, and this will be at a hub site.  However, when combined with SASE, the SD-WAN edge devices can be connected to a cloud-based infrastructure rather than the physical SD-WAN hubs. This brings the value of interconnectivity between branch sites without the complexity of deploying or managing physical Hub sites.

Zero Trust SASE: Vendor considerations

SASE features converge various individual components into one connected, cloud-delivered service, making it easy to control policies and behaviors. The SASE architecture is often based on a PoP design. When examining the SASE vendor, the vendor’s PoP layout should be geographically diverse, with worldwide entry and exit points. 

Also, considerations should be made regarding the vendor’s edge/physical infrastructure providers or colocation facilities. We can change your security posture, but we can’t change the speed of light and the laws of physics.

Consider how the SASE vendor routes traffic in their PoP fabric. Route optimization should be performed at each PoP. Some route optimizations are for high availability, while others are for performance. Does the vendor offer cold-potato or hot-potato routing? The cold-potato routing means bringing the end-user device into the provider’s network as soon as possible. On the other hand, “hot-potato routing” means the end user’s traffic traverses more of the public Internet.

The following is a list of considerations to review when discussing SASE with your preferred cybersecurity vendor:

A. Zero Trust SASE requirements: Information hiding:

Secure access service requires clients to be authenticated and authorized before accessing protected assets, regardless of whether the connection is inside or outside the network perimeter. Then, real-time encrypted connections are created between the requesting client and the protected asset. As a result, all SASE-protected servers and services are hidden from all unauthorized network queries and scan attempts.

You can’t attack what you can’t see.

The base for network security started by limiting visibility – you cannot attack what you cannot see. Public and private IP addresses range from separate networks. This was the biggest mistake we ever made as I.P. addresses are just binary, whether they are deemed public or private. If a host were assigned a public address and wanted to communicate with a host with a private address, it would need to go through a network address translation (NAT) device and have a permit policy set.

Understanding Port Knocking

Port knocking is a technique that enables secure and controlled access to network services. Traditionally, network ports are open and accessible, leaving systems vulnerable to unauthorized access. However, with port knocking, access to specific ports is only granted after a predefined sequence of connection attempts is made to other closed ports. This sequence acts as a virtual “knock” on the door, allowing authorized users to gain access while keeping malicious actors at bay.

To fully comprehend port knocking, let’s explore its inner mechanics. When users wish to access a specific service, they must first send connection attempts to a series of closed ports in a particular order. This sequence acts as a secret handshake, notifying the server that the user is authorized. Once the correct sequence is detected, the server dynamically opens the desired port, granting access to the requested service. It’s like having a hidden key that unlocks the door to a secure sanctuary.

Security based on the visibility

Network address translation is mapping an IP address space into another by modifying network address information in the IP header of packets while they are in transit across a traffic routing device. Limiting visibility this way works to a degree, but we cannot ignore the fact that a) if you have someone’s IP address, you can reach them, and b) if a port is open, you can potentially connect to it.

Therefore, the traditional security method can open your network wide for compromise, especially when bad actors have all the tools. However, finding, downloading, and running a port scanning tool is not hard.

“Nmap,” for Network Mapper, is the most widely used port scanning tool. Nmap works by checking a network for hosts and services. Once found, the software platform sends information to those hosts and services, responding. Nmap reads and interprets the response and uses the data to create a network map.

Example: Understanding Lynis

Lynis is an open-source security auditing tool for discovering vulnerabilities on Unix, Linux, and macOS systems. It comprehensively analyzes the system’s configuration and provides valuable insights into potential security weaknesses. By scanning the system against a vast database of known security issues, Lynis helps identify areas for improvement.

Lynis runs a series of tests and audits on the target system. It examines various aspects, including file permissions, system settings, available software packages, and network configurations. Lynis generates a detailed report highlighting any identified vulnerabilities or potential security gaps by analyzing these factors. This report becomes a valuable resource for system administrators and security professionals to take necessary actions and mitigate risks.

Example: Single Packet Authorization

Zero-trust network security hides information and infrastructure through lightweight protocols such as single-packet authorization (SPA). No internal IP addresses or DNS information is shown, creating an invisible network. As a result, we have zero visibility and connectivity, only establishing connectivity after clients prove they can be trusted to allow legitimate traffic. Now, we can have various protected assets hidden regardless of location: on-premise, public or private clouds, a DMZ, or a server on the internal LAN, in keeping with today’s hybrid environment.

Default-drop dynamic firewall

This approach mitigates denial-of-service attacks. Anything internet-facing is reachable on the public Internet and, therefore, susceptible to bandwidth and server denial-of-service attacks. The default-drop firewall is deployed, with no visible presence to unauthorized users. Only good packets are allowed. Single packet authorization (SPA) also provides for attack detection.

If a host receives anything other than a valid SPA packet or similar construct, it views that packet as part of a threat. The first packet to a service must be a valid SPA packet or similar security construct.

If it receives another packet type, it views this as an attack, which is helpful for bad packet detection. Therefore, SPA can determine an attack based on a single malicious packet, a highly effective way to detect network-based attacks. Thus, external network and cross-domain attacks are detected.

B. Zero Trust SASE architecture requirements: Mutually encrypted connections:

Transport Layer Security ( TLS ) is an encryption protocol that protects data when it moves between computers. When two computers send data, they agree to encrypt the information in a way they both understand. Transport layer security (TLS) was designed to provide mutual device authentication before enabling confidential communication over the public Internet. However, the standard TLS configuration validates that the client is connected to a trusted entity. So, typical TLS adoptions authenticate servers to clients, not clients to servers. 

Mutually encrypted connections

SASE uses the full TLS standard to provide mutual, two-way cryptographic authentication. Mutual TLS provides this and goes one step further to authenticate the client. Mutual TLS connections are set up between all components in the SASE architecture. Mutual Transport Layer Security (mTLS) establishes an encrypted TLS connection in which both parties use X. 509 digital certificates to authenticate each other.

MTLS can help mitigate the risk of moving services to the cloud and prevent malicious third parties from imitating genuine apps. This offers robust device and user authentication, as connections from unauthorized users and devices are mitigated. Secondly, forged certificates, which are attacks aimed at credential theft, are disallowed. This will reduce impersonation attacks, where a bad actor can forge a certificate from a compromised authority.

C. Need to know the access model: Zero Trust SASE architecture requirements

Thirdly, SASE employs a need-to-know access model. As a result, SASE permits the requesting client to view only the resources appropriate to the assigned policy. Users are associated with their devices, which are validated based on policy. Only connections to the specifically requested service are enabled, and no other connection is allowed to any other service. SASE provides additional information, such as who made the connection, from what device, and to what service.

This gives you complete visibility into all the established connections, which is hard to do without an IP-based solution. So now we have a contextual aspect of determining the level of risk. As a result, it makes forensics easier. The SASE architecture only accepts good packets; bad packets can be analyzed and tracked for forensic activities.

Key Point: Device validation

Secondly, it enforces device validation, which helps against threats from unauthorized devices. We can examine the requesting user and perform device validation. Device validation ensures that the machine runs on trusted hardware and is used by the appropriate user.

Finally, suppose a device becomes compromised. In that case, lateral movements are entirely locked down, as a user is only allowed access to the resource it is authorized to. Or they could be placed into a sandbox zone where human approval must intervene and assess the situation.

D. Dynamic access control: Zero Trust SASE architecture requirements

This traditional type of firewall is limited in scope as it cannot express or enforce rules based on identity information, which you can with zero trust identity. Attempting to model identity-centric control with the limitations of the 5-tuple, SASE can be used alongside traditional firewalls and take over the network access control enforcement that we try to do with conventional firewalls. SASE deploys a dynamic firewall that starts with one rule – deny all.

Then, requested communication is dynamically inserted into the firewall, providing an active firewall security policy instead of static configurations. For example, every packet hitting the firewall is inspected with a single packet authentication (SPA) and then quickly verified for a connection request. 

Key Point: Dynamic firewall

Once established, the firewall is closed again. Therefore, the firewall is dynamically opened only for a specific period. The connections made are not seen by rogues outside the network or the user domain within the network. Allows dynamic, membership-based enclaves that prevent network-based attacks.

The SASE dynamically binds users to devices, enabling those users to access protected resources by dynamically creating and removing firewall rules.  Access to protected resources is facilitated by dynamically creating and removing inbound and outbound access rules. Therefore, we now have more precise access control mechanisms and considerably reduced firewall rules.

**Micro perimeter**

Traditional applications were grouped into VLANs whether they offered similar services or not. Everything on that VLAN was reachable. The VLAN was a performance construct to break up broadcast domains, but it was pushed into the security world and never meant to be there. 

Its prime use was to increase performance. However, it was used for security in what we know as traditional zone-based networking. The segments in zone-based networks are too large and often have different devices with different security levels and requirements.

Key Points:

A. Logical-access boundary: SASE enables this by creating a logical access boundary encompassing a user and an application or set of applications. And that is it—nothing more and nothing less. Therefore, we have many virtual micro perimeters specific to the business instead of the traditional main inside/outside perimeter. Virtual perimeters allow you to grant access to the particular application, not the underlying network or subnet.

B. Reduce the attack surface: The smaller micro perimeters reduce the attack surface and limit the need for excessive access to all ports and protocols or all applications. These individualized “virtual perimeters” encompass only the user, the device, and the application. They are created and specific to the session and then closed again when it is over or if the risk level changes and the device or user needs to perform setup authentication.

C. Software-defined perimeter (SDP): SASE only grants access to the specific application at an application layer. The SDP part of SASE now controls which devices and applications can access distinctive services at an application level. Permitted by a policy granted by the SDP part of SASE, machines can only access particular hosts and services and cannot access network segments and subnets.

**Reduced: Broad Network Access**

Broad network access is eliminated, reducing the attack surface to an absolute minimum. SDP provides a fully encrypted application communication path. However, the binding application permits only authorized applications to communicate through the established encrypted tunnels, thus blocking all other applications from using them. This creates a dynamic perimeter around the application, including connected users and devices. Furthermore, it offers a narrow access path—reducing the attack surface to an absolute minimum.

E. Identity-driven access control: Zero Trust SASE architecture requirements

Traditional network solutions provide coarse-grained network segmentation based on someone’s IP address. However, someone’s IP address is not a good security hook and does not provide much information about user identity. SASE enables the creation of microsegmentation based on user-defined controls, allowing a 1-to-1 mapping, unlike with a VLAN, where there is the potential to see everything within that VLAN.

Identity-aware access: SASE provides adaptive, identity-aware, precision access for those seeking more precise access and session control to applications on-premises and in the cloud. Access policies are primarily based on user, device, and application identities. The procedure is applied independent of the user’s physical location or the device’s I.P. address, except where it prohibits it. This brings a lot more context to policy application. Therefore, if a bad actor gains access to one segment in the zone, they are prevented from compromising any other network resource.

Detecting Authentication Failures in Logs:

Syslog: Useful Security Technology

Syslog, short for System Logging Protocol, is a standard for message logging within computer systems. It collects various log entries from different sources and stores them in a centralized location. Syslog is a valuable resource for detecting security events as it captures information about system activities, errors, and warnings.

Auth.log is a specific type of log file that focuses on authentication-related events in Unix-based operating systems. It records user logins, failed login attempts, password changes, and other authentication activities. Analyzing auth.log can provide vital insights into potential security breaches, such as brute-force attacks or suspicious login patterns.

Now that we understand the importance of syslog and auth.log, let’s delve into some effective techniques for detecting security events in these files. One widely used approach is log monitoring, where automated tools analyze log entries in real time, flagging suspicious or malicious activities. Another technique is log correlation, which involves correlating events across multiple log sources to identify complex attack patterns.

Zero Trust SASE • Cloud Security Edge

SASE Secure Access Flow

SASE brings networking and security services together at distributed cloud edges. The user request is inspected and policy is enforced before reaching the protected application.

SECURE ACCESS PATH
Remote User Identity + device SASE PoP Distributed security edge Policy inspection Identity-aware enforcement Traffic optimization ZTNA Private applications SWG Web security CASB SaaS visibility FWaaS Cloud firewall Policy Inspect Allow / Block Protected App Only approved access
LEARNING PANEL
Selected

SASE PoP

A SASE Point of Presence brings security enforcement closer to users and applications.

ROLE Distributed security edge
TRAFFIC Identity-aware inspection
RESULT Only policy-approved traffic continues

Summary: Zero Trust SASE

Traditional security measures are no longer sufficient in today’s rapidly evolving digital landscape, where remote work and cloud-based applications have become the norm. Enter Zero Trust Secure Access Service Edge (SASE), a revolutionary approach that combines network security and wide-area networking into a unified framework. In this blog post, we explored the concept of Zero Trust SASE and its implications for the future of cybersecurity.

Understanding Zero Trust

Zero Trust is a security framework that operates under the “never trust, always verify.” It assumes no user or device should be inherently trusted, regardless of location or network. Instead, Zero Trust focuses on continuously verifying and validating identity, access, and security parameters before granting any level of access.

The Evolution of SASE

Secure Access Service Edge (SASE) represents a convergence of network security and wide-area networking capabilities. It combines security services, such as secure web gateways, firewall-as-a-service, and data loss prevention, with networking functionalities like software-defined wide-area networking (SD-WAN) and cloud-native architecture. SASE aims to provide comprehensive security and networking services in a unified, cloud-delivered model.

The Benefits of Zero Trust SASE:

a) Enhanced Security: Zero Trust SASE brings a holistic approach to security, ensuring that every user and device is continuously authenticated and authorized. This reduces the risk of unauthorized access and mitigates potential threats.

b) Improved Performance: By leveraging cloud-native architecture and SD-WAN capabilities, Zero Trust SASE optimizes network traffic, reduces latency, and enhances overall performance.

c) Simplified Management: A unified security and networking framework can streamline organizations’ management processes, reduce complexity, and achieve better visibility and control over their entire network infrastructure.

Implementing Zero Trust SASE

a) Comprehensive Assessment: Before adopting Zero Trust SASE, organizations should conduct a thorough assessment of their existing security and networking infrastructure, identify vulnerabilities, and define their security requirements.

b) Architecture Design: Organizations must design a robust architecture that aligns with their needs and integrates Zero Trust principles into their existing systems. This may involve deploying virtualized security functions, adopting SD-WAN technologies, and leveraging cloud services.

c) Continuous Monitoring and Adaptation: Zero Trust SASE is an ongoing process that requires continuous monitoring, analysis, and adaptation to address emerging threats and evolving business needs. Regular security audits and updates are crucial to maintaining a solid security posture.

Conclusion: Zero Trust SASE represents a paradigm shift in cybersecurity, providing a comprehensive and unified approach to secure access and network management. By embracing the principles of Zero Trust and leveraging the capabilities of SASE, organizations can enhance their security, improve performance, and simplify their network infrastructure. As the digital landscape continues to evolve, adopting Zero Trust SASE is not just an option—it’s necessary to safeguard our interconnected world’s future.

microservices development

Microservices Observability

Monitoring Microservices

As applications move from monolithic architectures toward distributed services, understanding what is happening inside the system becomes increasingly difficult. A single user request may travel through an API gateway, several microservices, databases, message queues, and external services before a response is returned. When something goes wrong, knowing that the application is slow is not enough; engineering teams need to understand where the failure occurred, how services were affected, and what changed.

Observability provides the telemetry and context needed to answer those questions. In a microservices environment, observability helps teams understand system behaviour from the outputs produced by the applications and infrastructure. Rather than monitoring individual services in isolation, teams can examine relationships between services, dependencies, requests, errors, resource consumption, and user-facing performance.

Distributed tracing is particularly valuable for following a request across service boundaries. A trace can show the sequence of operations involved in a transaction, including which services were called, how long each operation took, and where delays or failures occurred. This makes it possible to investigate problems that would be difficult to identify from a single service’s logs or metrics.

Logs provide another layer of context. Application and infrastructure events can be collected centrally and correlated with other telemetry, making it easier to investigate errors, configuration changes, authentication events, and unusual behaviour. Structured logging is especially useful because consistent fields and formats allow logs to be searched and analysed alongside traces and metrics.

Metrics provide the quantitative view of system health. Request rates, latency, error rates, resource utilization, queue depth, and service availability can reveal trends and changes in system behaviour. When these measurements are connected to appropriate thresholds and service objectives, teams can detect degradation before it becomes a larger application or user-experience problem.

Effective observability also depends on instrumentation and standardization. Services need to produce meaningful telemetry, and that telemetry needs consistent naming, metadata, correlation identifiers, and collection practices. Open standards such as OpenTelemetry can help organizations instrument distributed applications without tightly coupling telemetry to a single observability platform.

Automation is equally important as environments grow. Telemetry collection, alerting, dashboards, anomaly detection, and incident workflows should operate consistently across services and environments. Observability should also extend beyond application code to the infrastructure, containers, orchestration platforms, databases, networks, and cloud services that applications depend upon.

The ultimate objective is not simply to collect more data. Effective observability turns telemetry into operational understanding. By correlating traces, metrics, logs, dependencies, and infrastructure signals, engineering teams can move from discovering that something is broken to understanding why it happened and what impact it has. That makes observability a fundamental capability for operating reliable, scalable, and continuously evolving microservices architectures.

Microservices • Observability

Microservices Observability Explorer

Understand how metrics, logs and distributed traces work together to reveal what is happening inside a distributed application.

OBSERVABILITY SIGNALS
Microservices Application Distributed services and dependencies Metrics Rate • Errors • Latency Saturation • Throughput Logs Events • Errors • State Detailed forensic data Distributed Traces Trace ID • Spans End-to-end request path Unified Observability Correlate metrics + logs + traces Investigate the Failure Detect • correlate • explain
LEARNING PANEL
Selected

Unified Observability

Observability becomes powerful when different telemetry signals are correlated rather than examined independently.

QUESTION What is happening?
SIGNAL Metrics + logs + traces
VALUE Connect symptoms to causes

Highlights: Monitoring Microservices

**Understanding Microservices Observability**

Before we dive deeper, let’s establish a clear understanding of what microservices observability entails. Observability refers to gaining insights into a system’s internal state based on its external outputs. In microservices, observability involves collecting and analyzing data from various sources, such as logs, metrics, and traces, to understand the system’s behavior and performance comprehensively.

Key Points:

– Focusing on key components that enable comprehensive monitoring and troubleshooting is crucial to achieving effective observability. These components include logging, metrics, and distributed tracing. Logging provides a detailed record of events and system activities, while metrics measure quantitative system performance. Distributed tracing allows tracking requests propagating through multiple microservices, giving valuable insights into the latency and dependencies between services.

– Numerous tools and technologies have emerged to support the observability of microservices. Prominent examples include popular open-source solutions like Prometheus, Grafana, and Jaeger. These tools provide capabilities for collecting, storing, visualizing, and analyzing observability data. Additionally, cloud-based platforms like AWS CloudWatch and Azure Monitor offer managed services that simplify the setup and management of observability infrastructure.

– Microservices monitoring is suitable for known patterns that can be automated, while microservices observability is suitable for detecting unknown and creative failures. Microservices monitoring is a critical part of successfully managing a microservices architecture. It involves tracking each microservice’s performance to ensure there are no bottlenecks in the system and that the microservices are running optimally.

The Need for Microservices Observability:

1. Obfuscation: When creating microservices, your application becomes more distributed, the coherence of failures decreases, and we live in a world of unpredictable failure mode—also, the distance between cause and effect increases. For example, an outage at your cloud provider’s blob storage could cause huge cascading latency for everyone. In today’s environment, we have new cascading problems.

2. Inconsistency and highly independent: Distributed applications might be reliable, but the state of individual components can be much less consistent than in monolithic or non-distributed applications, which have elementary and well-known failure modes. In addition, each element of a distributed application is designed to be highly independent, and each component can be affected by different upstream and downstream components.

3. Decentralization: How do you look for service failures when a thousand copies of a service may run on hundreds of hosts? How do you correlate those failures so you can understand what’s going on?

Microservices Observability — Analytics for Distributed Systems

Microservices observability is the practice of instrumenting distributed systems so engineers can infer internal state from external outputs. As architectures scale across Kubernetes clusters and cloud regions, traditional monitoring fails because no single log or metric reveals the full story. Observability requires metrics, logs, and traces working together, unified by shared correlation IDs. This aligns with industry guidance that debugging microservices without observability is “effectively guessing” because failures propagate across multiple services and dependencies.

Metrics — High‑Level Health Signals

Metrics provide aggregated numeric measurements over time such as request rate, error rate, and latency percentiles. They serve as the first line of defense for alerting and dashboards. Metrics are ideal for identifying symptoms like p99 latency spikes or rising error rates. Tools such as Prometheus, Grafana, and Datadog are commonly used to scrape and visualize these metrics. Metrics alone cannot explain why a request failed, but they reveal where to start investigating.

Logs — Detailed Forensic Records

Logs capture discrete events such as errors, state changes, and important actions. In microservices, structured logging (JSON with consistent fields) is mandatory because plain text logs become unusable at scale. Logs should include timestamps, service names, trace IDs, and contextual metadata. Centralized log aggregation platforms like ELK, Loki, or cloud‑native logging systems allow engineers to pivot quickly during incident response. Logs help answer “what happened,” but without traces, they cannot reconstruct cross‑service request flows.

Traces — End‑to‑End Request Journey

Distributed tracing is the cornerstone of microservices observability. A unique trace ID is attached to each incoming request, and every service creates spans that record timing, operations, and status. These spans form a tree that reconstructs the full lifecycle of the request across all service boundaries. The W3C Trace Context standard defines how trace IDs propagate via traceparent and tracestate headers, enabling consistent correlation across services. Tools such as Jaeger, Zipkin, and Tempo visualize these traces, revealing bottlenecks, cascading failures, and dependency issues that metrics and logs alone cannot expose.

Unified Observability — Correlation Across All Signals

True observability emerges only when metrics, logs, and traces are correlated using shared context identifiers. Metrics show that latency spiked, logs show the error messages, and traces reveal which service caused the slowdown and how it propagated. This unified approach is essential because microservices failures are often subtle—partial degradation, retry storms, queue buildup, or downstream dependency issues. Observability allows teams to debug unknown failures, optimize performance, and maintain reliability across distributed systems.

Cloud Monitoring: Compute Engine & Ops Agent

What is an Ops Agent?

Ops Agent is a monitoring agent provided by Google Cloud that allows users to collect and export monitoring data from their Compute Engine instances. It acts as a bridge between your virtual machines and the Google Cloud Monitoring service, enabling real-time visibility into the health and performance of your infrastructure.

Ops Agent Advantages:

Ops Agent offers several advantages when it comes to monitoring a Compute Engine. Firstly, it provides a unified solution for collecting metrics, logs, and events from your instances. This means you can easily access and analyze all the necessary data in one centralized location. Additionally, Ops Agent offers resource-efficient monitoring, minimizing the impact on your instances’ performance while providing accurate and timely information.

Implementing Ops Agent:

To start monitoring your Compute Engine instances with Ops Agent, you need to follow a few simple steps. First, ensure that you have the necessary permissions and enable the necessary APIs in your Google Cloud project. Then, install Ops Agent on your instances using the provided installation script or by creating a custom image with Ops Agent pre-installed. Finally, configure the agent to collect the desired metrics, logs, and events based on your monitoring requirements.

**Tools of the past: Log data and series statistics**

Traditionally, microservices monitoring has boiled down to two types of telemetry data: log data and time series statistics. Time series data is also known as metrics, as to make sense of a metric, you need to view a period. However, as we broke the software into tiny, independently operated services and distributed those fragmented services, the logs, and metrics we captured told you very little of what was happening to the critical path.

Understanding the critical path is the most important thing, as this is what the customer is experiencing. Looking at a single stack trace or watching CPU and memory utilization on predefined graphs and dashboards is insufficient. As software scales in-depth but breadth, telemetry data like logs and metrics alone don’t provide clarity; you must quickly identify production problems.

Components – Microservices Monitoring

Metrics 

A. Metrics: This includes tracking metrics such as response time, throughput, and error rate. This information can be used to identify performance issues or bottlenecks. By collecting and interpreting metrics, organizations gain valuable insights into their microservices-based applications, enabling them to make informed decisions and proactively address potential problems.

Logs 

B. Logs: Logging allows administrators to track requests, errors, and exceptions, which can provide deeper insight into the performance of microservices architecture. Logs provide a unique perspective by capturing valuable information about system events and activities. Logs act as a breadcrumb trail, documenting the inner workings of microservices.

One can detect anomalies, identify bottlenecks, and troubleshoot errors effectively by analyzing logs. Capturing log data from each microservice and centralizing it in a log management system allows comprehensive monitoring across the entire architecture. Logs can reveal valuable insights such as response times, error rates, and resource usage, empowering teams to make data-driven decisions.

Tracing 

C. Tracing: Tracing provides a timeline of events within the system. This can be used to identify the source of issues or to track down errors. In microservices, tracing refers to capturing and analyzing the flow of requests as they traverse through different services. By tracing requests, we can gain valuable insights into the performance and behavior of our microservices architecture. From identifying latency issues to detecting errors and bottlenecks, tracing provides a holistic view of the entire request journey.

Alerts

D. Alerts: Alerts notify administrators when certain conditions are met. For example, administrators can be alerted if a service is down or performance is degrading . Configuring alerting rules is a critical step in microservices monitoring. It involves defining thresholds or conditions that, when breached, trigger alerts. These rules should be set based on the specific requirements of each microservice, considering factors like expected response time, error rates, or resource thresholds. Additionally, it’s essential to determine the appropriate severity levels for different alerts.

Finally, it is essential to note that microservices monitoring is not just limited to tracking performance. It can also detect security vulnerabilities and provide insights into the architecture.

By leveraging microservices monitoring, organizations can ensure that their microservices architecture runs smoothly and that any issues are quickly identified and resolved. This can help ensure the organization’s applications remain reliable and secure.

Example Product: Cisco AppDynamics

### Why Choose Cisco AppDynamics?

Cisco AppDynamics stands out in the crowded APM market for several compelling reasons. First, it offers end-to-end visibility into your application’s performance, from the end-user experience down to the underlying infrastructure. This comprehensive view allows you to pinpoint issues quickly and resolve them before they impact your users. Additionally, AppDynamics employs machine learning algorithms to detect anomalies and provide actionable insights, enabling proactive problem-solving.

### Key Features of Cisco AppDynamics

One of the standout features of AppDynamics is its ability to automatically map your application’s topology, giving you a clear picture of how different components interact. This dynamic mapping is invaluable for troubleshooting and optimizing your application. Another key feature is its robust alerting system, which notifies you of performance issues in real-time, allowing for immediate intervention. Furthermore, AppDynamics offers detailed analytics and reporting capabilities, helping you make data-driven decisions to improve your application’s performance.

### Integrations and Extensibility

Cisco AppDynamics is designed to integrate seamlessly with a wide range of technologies and platforms, making it a versatile tool for any IT environment. Whether you’re using cloud services like AWS or Azure, container orchestration platforms like Kubernetes, or traditional on-premise infrastructure, AppDynamics has you covered. The platform also supports custom extensions, allowing you to tailor it to your specific needs and workflows.

### Real-World Use Cases

Many organizations have successfully leveraged Cisco AppDynamics to achieve significant improvements in their application performance and user experience. For instance, a leading e-commerce company used AppDynamics to identify and resolve a critical bottleneck in their checkout process, resulting in a 20% increase in conversion rates. Similarly, a financial services firm utilized AppDynamics’ machine learning capabilities to predict and prevent potential outages, ensuring uninterrupted service for their customers.

Example: What are VPC Flow Logs?

VPC Flow Logs provide detailed information about the IP traffic flowing through your Virtual Private Cloud (VPC). They capture metadata about each network flow, including source and destination IP addresses, ports, protocol, packet and byte counts, and more. Enabling VPC Flow Logs allows you to gain visibility into the traffic patterns within your VPC, allowing you to monitor, troubleshoot, and analyze network activity.

VPC Logs & Cloud Monitoring

Google Cloud offers a variety of powerful tools for analyzing VPC Flow Logs and extracting meaningful insights. One such tool is Cloud Logging, which allows you to view and search flow logs in real-time, set up alerts and notifications, and create custom dashboards for visualization. Additionally, you can leverage BigQuery, Google Cloud’s data warehouse solution, to store and analyze large volumes of flow log data using SQL queries and advanced analytics techniques.

 

Monitoring Microservices

Microservices Observability • Widget 2

Distributed Trace / Critical Path Lab

Follow one request across multiple microservices and see how distributed tracing identifies latency, failures and the critical path.
Request Trace
Client Request GET /orders/1234 API Gateway Ingress / Routing Auth Service Identity / Token Check Order Service Business Logic Payment / Database Downstream Dependency 18 ms 12 ms 35 ms 25 ms
Normal span
Latency problem
Error
● TRACE ACTIVE
Client Request
The request enters the microservices environment and receives a distributed trace identifier. Every downstream service creates a child span using the same trace context.
Span
Root span
Duration
0 ms
Relationship
Parent request
Observation
Request begins the trace.
trace-id: 4bf92f3577b34da6a3ce929d0e0e4736

Microservices Monitoring and Observability

– Containers, cloud platforms, scalable microservices, and the complexity of monitoring distributed systems have highlighted significant gaps in the microservices monitoring space that have been static for some time. As a result, you must fully understand performance across the entire distributed and complex stack, including distributed traces across all microservices.

– So, to do this, you need a solution that can collect, process, and store data used for monitoring. And the data needs to cover several domains and then be combined and centralized for analysts.

– This can be an all-in-one solution that represents or bundles different components for application observability. The bundled solutions would be, for example, an Application Performance Monitoring (APM) that consists of application performance monitoring tools or a single platform, such as Prometheus, which lives in a world of metrics only.  

Application Performance Monitoring:

Application performance monitoring typically involves tracking an application’s response time, the number of requests it can handle, and the amount of memory or other system resources it uses. This data can be used to identify any issues with application performance or scalability. Organizations can take corrective action by monitoring application performance to improve the user experience and ensure their applications run as efficiently as possible.

Identify Trends & Patterns:

Application performance monitoring also helps organizations better understand their users by providing insight into how applications are used and how well they are performing. In addition, this data can be used to identify trends and patterns in user behavior, helping organizations decide how to optimize their applications for better user engagement and experience.

Monitoring observability
Diagram: Monitoring Observability. Source is Bravengeek

**Microservices Monitoring Categories**

We have several different categories to consider. For microservices monitoring and Observability, you must first address your infrastructures, such as your network devices, hypervisors, servers, and storage. Then, you should manage your application performance and health.

Then, you need to monitor how to manage network quality and optimize when possible. For each category, you must consider white box and black box monitoring and potentially introduce new tools such as Artificial Intelligence (AI) for IT operations (AIOps).

Prevented approach to Microservice monitoring: AI and ML.

When choosing microservices observability software, consider a more preventive approach than a reactive one that is better suited for traditional environments. Prevented approaches to monitoring can use historical health and performance telemetry as an early warning with the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques.

Whitebox Monitoring

White box monitoring offers more details than a black box, which tells you something is broken without telling you why. White box monitoring details the why, but you must ensure the data is easily consumable. Black box microservices monitoring can help with predictable failures and known failure modes. Still, given the creative ways that applications and systems fail today, we must examine the details of white-box microservices monitoring. Complex applications fail in unpredictable ways, often termed black holes.

New failures & failure modes

Distributing your software presents new types of failure, and these systems can fail in creative ways and become more challenging to pin down. For example, the service you’re responsible for may be receiving malformed or unexpected data from a source you don’t control because a team manages that service halfway across the globe.

White box monitoring takes a different approach from black box monitoring. It uses Instrumentation, which exposes details about the system’s internals to help you explore these black holes and better understand the creative mode in which applications fail today.

Example: Application Latency

Application latency refers to the time it takes for an application to respond to a user’s request. It is influenced by various factors such as network latency, processing time, and database queries. Monitoring and analyzing application latency can help identify bottlenecks and optimize performance.

Google Cloud Service Mesh

**What is a Cloud Service Mesh?**

A cloud service mesh is a configurable infrastructure layer for microservices applications that makes communication between service instances flexible, reliable, and fast. It typically includes a set of network proxies deployed alongside application code, which handle tasks such as load balancing, service discovery, and authentication. The service mesh enables developers to focus on the business logic while the mesh handles communication concerns.

**Key Features and Benefits**

1. **Improved Security**: One of the main advantages of a service mesh is its ability to enhance security. By managing service-to-service authentication, authorization, and encryption, it ensures that data is protected during transit.

2. **Observability**: A service mesh provides comprehensive observability through metrics, logs, and traces. This enables better monitoring and troubleshooting, helping teams identify and resolve issues quickly.

3. **Traffic Management**: Service meshes allow for sophisticated traffic management capabilities, such as load balancing, traffic splitting, and fault injection. This ensures high availability and resilience of applications.

**Google’s Approach to Service Mesh**

Google has been a pioneer in developing service mesh technology, with its flagship product, Istio. Istio is an open-source service mesh that provides a uniform way to secure, connect, and monitor microservices. Google Cloud Platform (GCP) integrates Istio to offer these capabilities as part of its suite of managed services. This integration allows developers to leverage the power of service mesh without the operational overhead of managing it themselves.

**Case Studies and Real-World Applications**

Several organizations have successfully implemented Google’s service mesh solutions to optimize their operations. For instance, e-commerce giants and financial institutions have seen significant improvements in their system reliability and security by using Istio on GCP. These real-world applications highlight the practical benefits and transformative potential of adopting a service mesh.

Introducing Cloud Trace

Cloud Trace is a comprehensive performance analysis tool provided by Google Cloud. It allows developers to trace and visualize the latency of requests across their applications. By collecting detailed information about each request, including timing data and associated events, Cloud Trace offers valuable insights into application performance.

Microservices Observability: Techniques

Collection, storage, and analytics: Regardless of what you are monitoring, the infrastructure, or the application service, monitoring requires 3 three inputs, more than likely across three domains. We require:

    1. Data collection, 
    2. Storage, and 
    3. Analysis.

We need to look at metrics, traces, and logs for these three domains or, let’s say, components. Out of these three domains, trace data is the most beneficial and excellent way to isolate performance anomalies for distributed applications. Trace data falls into distributed tracing brackets, enabling flexible consumption of capture traces. 

First, you must establish a baseline comprising the four golden signals – latency, traffic, errors, and saturation. The golden signals are good indicators of health and performance and apply to most components of your environment, such as the infrastructure, applications, microservices, and orchestration systems.

I recommend automating this baseline and the automation alerts for deviations from baselines. However, if you collect too much data, you may be alerted to too much. Service Level Indicators (SLI) can help you determine what to alert about and what matters to the user experience. 

The Effect on Microservices: Microservices Monitoring

When considering a microservice application, many believe this independent microservice is independent, but this is nothing more than an illusion. These microservices are highly interdependent, and a failure or slowdown in one service propagates across the stack of microservices.

A typical architecture may include a backend service, a front-end service, or maybe even a docker-compose file. So, several containers must communicate to carry out operations. 

For a simple microservice architecture, we would have a simple front end minimizing a distributed application setup, where microservices serving static contents are at the front end. At the same time, the heavy lifting is done with the other service.   

**Monolith and microservices monitoring**

We have more components to monitor than we had in the monolithic world. With their traditional monolithic, there are only two components to monitor. Then, we had the applications and the hosts.

Compared to the cloud-native world, we have containerized applications orchestrated by Kubernetes with multiple components requiring monitoring. These components include the hosts, Kubernetes platform, Docker containers, and containerized microservices.

**Distributed systems have different demands**

Today, distributed systems are the norm, placing different demands on your infrastructure than the classic, three-tier application. Pinpointing issues in a microservices environment is more challenging than with a monolithic one, as requests traverse both between different layers of the stack and across multiple services. 

**The Challenges: Microservices**

The things we love about microservices are independence and idempotence, which make them difficult to understand, especially when things go wrong. As a result, these systems are often referred to as deep systems, not due to their width but their complexity.

We can no longer monitor their application by using a script to access the application over the network every few seconds, report any failures, or use a custom script to check the operating system to understand when a disk is running out of space.

Understanding saturation is an implemented signal, but it’s just one of them. It quickly becomes unrealistic for a single human, or even a group, to understand enough of the services in the critical path of even a single request and continue maintaining it. 

**Node Affinity or Taints**

Microservices-based applications are typically deployed on dynamic and transient containers. This leaves an unpredictable environment where the pods get deployed and run unless specific intent is expressed using affinity or taints. However, pod placement can still be unpredictable. The unpredictable nature of pod deployment and depth of configuration can lead to complex troubleshooting.

Understanding GKE-Native Monitoring

Prometheus Integration

GKE-Native Monitoring provides a comprehensive and real-time view of the health and performance of your Kubernetes workloads. Leveraging built-in Prometheus integration enables automatic metrics collection and aggregation, offering deep insights into resource utilization, latency, errors, and more. With GKE-Native Monitoring, developers can quickly identify bottlenecks, optimize resource allocation, and proactively detect and troubleshoot issues before they impact users.

Stackdriver Logging

GKE-Native Monitoring integrates with Stackdriver Logging, Google Cloud’s powerful log management and analysis tool. By combining metrics and logs in a unified platform, developers and operators gain complete application observability. Stackdriver Logging provides advanced filtering and querying capabilities, allowing users to search and analyze logs across multiple Kubernetes clusters quickly. With log-based metrics and alerts, teams can set up proactive monitoring to detect anomalies or specific events, ensuring the reliability and performance of their applications.

The Beginnings of Distributed Tracing

Introducing Distributed Tracing

Distributed tracing is used in microservices and other distributed applications because a single operation touches many services. It is a type of correlated logging that helps you gain visibility into the process of a distributed software system. Distributed tracing consists of collecting request data from the application and then analyzing and visualizing this data as traces.

Tracing data, in the form of spans, must be collected from the application, transmitted, and stored to reconstruct complete requests. This can be useful for performance profiling, debugging in production, and root causes analysis of failures or other incidents. 

A key point: The value of distributed tracing

Distributed tracing allows you to understand what a particular service is doing as part of the whole. Thus providing visibility into the operation of your microservice architecture. The trace data you generate can display the overall shape of your distributed system and view individual service performance inside a single request.

**Distributed Tracing Components** 

  1. What is a trace?

Consider your software in terms of requests. Each component of your software stack works in response to a request or a remote procedure call from another service. So, we have a trace encapsulating a single operation within the application, end to end, and represented as a series of spans. 

Each traceable unit of work within the operations generates a span. You can get trace data in two ways: through the Instrumentation of your service processes or by transforming existing telemetry data into trace data. 

  1. Introducing a SPAN

We call each service’s work a span, as in the period it takes for the work to occur. These spans can be annotated with additional information, such as attributes, tags, or logs. So, we can have a combination of metadata and events that can be added to spans—creating effective spans that unlock insights into the behavior of your service. The span data produced by each service is then forwarded to some external process, where it can be aggregated into a trace, analyzed, and stored for further insights.

**Example: Open Tracing**

When you are ready to get started with distributed tracing, you will come across OpenTracing. OpenTracing is a set of standards exposed as frameworks. It’s a vendor-neutral API and Instrumentation for distributed tracing. 

Open tracing does not give you the library but rather a set of rules and extensions that another library can adopt. Thus, you can use and swap around different libraries and expect the same things. 

Diagram: Distributed Tracing Example. Source is Simform

Microservices Architecture Example

Let’s examine an example of the request library for Python. So we have Requests, an elegant and simple HTTP library for Python. The request library talks to HTTP and will rely on specific standards; the standard here will be HTTP. So in Python, when making a “requests.get”.

The underlying library implementation will make a formal HTTP request using the GET method. Thus, the HTTP standards and specs lay the ground rules for what is expected from the client and the server.

The OpenTracing projects do the same thing. They set out the ground rules for distributed tracing, regardless of the implementation and the language used. They have several liabilities available in nine languages: Go, JavaScript, Java, Python, Ruby, PHP, Objective-C, and C++.

For example, the OpenTracing API for Python implements open tracing. This set of standards for tracing with Python provides examples of what Instrumentation should look like and common ways to start a trace. 

Connect the dots with distributed tracing.

And this is a big difference in why you would use tracing and logging. Tracing allows you to connect the dots from one end of the application to the other end of the application. So, if you are starting a request on the front end and want to see how that works on the backend, that works. A trace and child traces connected will have a representation. 

Visual Representation with Jaeger: You may need to use Jaeger for the visual representation. Jaeger is an open-source end-to-end visual representation of tracing that allows you to monitor and troubleshoot transactions in complex distributed systems.

So, we have a dashboard where we can interact and search for traces. Jaeger addresses problems such as monitoring distributed tracing, performance and latency optimizations, root cause analysis, service dependency analysis, and distributed content propagation. It has clients in different languages.

So, for example, if you are using Python, there will be client library features for Python. 

The Role of OpenTelementry

OpenTelementry: We also have OpenTelementry, which is similar. It is described as an observability framework for cloud-native software and is in beta across several languages. It is geared towards traces, metrics, and logs, so it does more than OpenTracing. 

Observability means a system’s internal states can be inferred from its external outputs. Therefore, the tools used to complete an Observability system help understand the relationships between causes and effects in distributed systems.

The term Observability is borrowed from the control theory. It suggests a holistic, data-centric view of microservices monitoring that enables exploration capabilities and identifying unknown failures with the more traditional anomaly detection and notification mechanisms.

Goal: The ultimate goal of Observability is to :

  • Improving baseline performance
  • Restoring baseline performance (after a regression)

By improving the baseline, you improve the user experience. This could be because performance often means request latency for user-facing applications. Then, we have performance regressions, including application outages, which can result in a loss of revenue and negatively impact the brand. The regressions’ time accepted comes down to user expectations. What is accessible, and what is in the SLA?

With chaos engineering tests, you understand your limits and the new places where your system and applications can be made. Chaos Engineering helps you know your system by introducing controlled experiments when debugging microservices. 

A key point: Massive amount of data

Remember that instrumenting potentially generates massive amounts of data, which can cause challenges in storing and analyzing. You must collect, store, and analyze data across the metrics, traces, and logs domains. Then, you need to alert me to these domains and what matters most, not just when an arbitrary threshold is met.

The role of metrics: Most people know a metric comprising a value, timestamp, and metadata. Metrics are collections of statistics that need to be analyzed over time. A single instance of a metric is of limited value. Examples include request rate, average duration, and queue size. These values are usually captured as time series so that operators can see and understand changes to metrics over time. 

Add labels to metric: We can add labels as key-value pairs to better understand metrics. The labels add additional context to this data point. So, the label is a key-value pair indexed with the metrics as part of the injection process. In addition, metrics can now be broken down into sub-metrics.

As we enter the world of labels and tags for metrics, we need to understand the effects this may have on Cardinality. While each indexed label value adds time series, this will come at storage and processing costs. Therefore, we use Cardinality to understand the impact of labels on a metric store.

Aggregated metrics: I continue to see the issue that metrics are typically aggregated every minute or even six to twelve times per minute. However, metrics must be aggregated and visualized within at most one minute but ideally even more quickly. Key questions are: What is the window across which values are aggregated? How are the windows from different sources aligned?

A key point: The issues of Cardinality

Aggregated Metrics allow you to get an aggregate understanding of what’s happening to all instances of a given service and even narrow your query to specific groups of services but fail to account for infinite Cardinality. Due to issues with “high-cardinality” within a time series storage engine, it is recommended to use labels rather than hierarchical naming for metrics.

Prometheus Monitoring

Examples: Push and Pull

So, to get metric, you need to have a push or pull approach. A push agent transmits data upstream and, more than likely, on a scheduled basis. A pull agent expects to be polled. Then, we have Prometheus and several Prometheus metric types. We have a Prometheus server with a pull approach that fits better into larger environments.

Prometheus does not use the term agent and has what is known as exporters. They allow the Prometheus server to pull metrics back from software that cannot be instrumented using the Prometheus client libraries.

a) Prometheus Kuberentes

Prometheus Kubernetes is an open-source monitoring platform that originated at SoundCloud and was released in 2012. Its capabilities include metric collection, storage, data analyses, and visualizations. For visualizations, we can use Prometheus and Grafana.

b) Storing Metrics

You can sort metrics that are time-series data in a general-purpose relational database. However, they should be stored in an optimized repository for storing and retrieving time-series data. We have several time-series storage options, such as Altas, InfluxDB, and Prometheus. Prometheus stands out, but keep in mind that, as far as I’m aware, there is no commercial support and limited professional services for Prometheus.

c) The Role of Logs

Then, we have highly detailed logs. Logs can be anything, unlike metrics, which have a daily uniform format. However, logs do provide you with why something is broken. Logs capture activity that can be printed to the screen or sent to a backend to be centrally stored and viewed.

There is very little standard structure to logs apart from a timestamp indicating when the event occurred. There is minimal log schema, and log structure will depend on how the application uses it and how developers create logs.

d) Emitting Logs

Logs are emitted by almost every entity, such as the basic infrastructure, network and storage, servers and computer notes, operating system nodes, and application software. So, there are various log sources and several tools involved in transport and interpretation, making log collection a complex task. However, remember that you may assume a large amount of log data must be stored.

Search engines such as Google have developed several techniques for searching extensive datasets using arbitrary queries, which have proved very efficient. All of these techniques can be applied to log data.  

e) Logstash, Beats, and FluentD

Logstash is a cloud-scale ingestion tool and is part of the Elasticsearch suit. However, there have been concerns with the performance and scalability of Logstash, which brings us to the lightweight version of Beats. So, if you don’t need the sophisticated data manipulation and filtering of Logstash, you can use Beasts. FluentD provides a unified logging layer or a way to aggregate logs from many different sources and distribute them to many destinations with the ability to transform data.

f) Storing Logs

Structure data such as logs and events are made of key-value pairs, any of which may be searched. This leads us to repositories called nonrelational or no SQL databases. So, storing logs represents a different storage problem from that of metrics. Examples of KV databases include Memcache and Redis.

However, they are not a good choice for log storage due to the inefficiency of indexing and searching. The ELK stack has an indexing and searching engine, a collector, a Logstash, a visualization tool, and the dominant storage mechanism for soft log and event data.

A key point: Analyze logs with AI

So, once you store the logs, they need to be analyzed and viewed. Here, you could, for example, use Splunk. Its data analysis capabilities range from security to AI for IT operations (AIOps). Kibana, part of the Elastic Stack, can also be used.

Microservices • Incident Investigation

Microservices Incident Investigation Lab

Start with a performance symptom and correlate telemetry until the likely root cause becomes visible.

INCIDENT INVESTIGATION
Production Symptom p99 latency increased Metrics Detect the abnormal signal Distributed Trace Locate the slow service API Service Normal • 24ms Order Service Elevated • 210ms Payment Service Normal • 31ms Service Logs Database timeout detected
INVESTIGATION PANEL
Investigation

Latency Spike

The application is experiencing increased p99 latency. Start with the metric, then follow the trace to identify where the latency is being introduced.

SYMPTOM p99 latency increased
LOCATION Order Service
EVIDENCE Database timeout in service logs
LIKELY CAUSE Slow database dependency

Summary: Monitoring Microservices

Monitoring microservices has become a critical aspect of maintaining the performance and reliability of modern applications. With the increasing adoption of microservices architecture, understanding how to monitor and manage these distributed systems effectively has become indispensable. In this blog post, we explored the key considerations and best practices for monitoring microservices.

The Need for Comprehensive Monitoring

Microservices are highly distributed and decentralized, which poses unique challenges regarding monitoring. Traditional monolithic applications are more accessible to monitor, but microservices require a different approach. Understanding the need for comprehensive monitoring is the first step toward ensuring the reliability and performance of your microservices-based applications.

Choosing the Right Monitoring Tools

This section will delve into the various monitoring tools available for monitoring microservices. From open-source solutions to commercial platforms, there is a wide range of options. We will discuss the critical criteria for selecting a monitoring tool: scalability, real-time visibility, alerting capabilities, and integration with existing systems.

Defining Relevant Metrics

To effectively monitor microservices, it is essential to define relevant metrics that provide insights into the health and performance of individual services as well as the overall system. In this section, we will explore the key metrics to monitor, including response time, error rates, throughput, resource utilization, and latency. We will also discuss the importance of setting appropriate thresholds for these metrics to trigger timely alerts.

Implementing Distributed Tracing

Distributed tracing plays a crucial role in understanding the flow of requests across microservices. By instrumenting your services with distributed tracing, you can gain visibility into the entire request journey and identify bottlenecks or performance issues. We will explore the benefits of distributed tracing and discuss popular tracing frameworks like Jaeger and Zipkin.

Automating Monitoring and Alerting

Keeping up with the dynamic nature of microservices requires automation. This section will discuss the importance of automated monitoring and alerting processes. From automatically discovering new services to scaling monitoring infrastructure, automation plays a vital role in ensuring the effectiveness of your monitoring strategy.

Conclusion:

Monitoring microservices is a complex task, but with the right tools, metrics, and automation in place, it becomes manageable. By understanding the unique challenges of monitoring distributed systems, choosing appropriate monitoring tools, defining relevant metrics, implementing distributed tracing, and automating monitoring processes, you can stay ahead of potential issues and ensure optimal performance and reliability for your microservices-based applications.

Cisco ACI

Cisco ACI | ACI Infrastructure

Cisco ACI | ACI Infrastructure

Modern data centre networks are no longer designed simply around VLANs, subnets, and device-by-device configuration. Applications are distributed across physical servers, virtual machines, containers, private infrastructure, and cloud environments, creating a much more complex set of connectivity and security requirements. Cisco Application Centric Infrastructure (ACI) approaches this problem by making application requirements and policy a central part of how the data centre network is designed and operated.

Cisco ACI combines a physical network fabric with a software-defined policy and management framework. Rather than requiring administrators to configure every switch and network service independently, ACI allows connectivity, segmentation, and application relationships to be expressed through policy. The underlying fabric then provides the network connectivity required to implement that policy. This separation between application intent and individual device configuration is one of the fundamental ideas behind ACI.

The application-centric model changes the way network requirements are represented. Instead of thinking only in terms of individual ports, VLANs, and IP addresses, administrators can define applications through groups of related workloads and describe how those workloads should communicate. Policies can then specify which application components are permitted to communicate, what services they require, and which security boundaries should exist between them.

Automation is another important part of the architecture. Once application and network policies have been defined, ACI can apply those policies consistently across the fabric. This reduces repetitive configuration and helps limit configuration drift as environments grow. It also provides a more structured foundation for integration with orchestration platforms, automation systems, and operational workflows.

Security is closely connected to the policy model. ACI can provide segmentation between application groups and workloads, allowing organisations to establish more granular communication boundaries than a traditional flat network. Microsegmentation can help reduce unnecessary lateral communication, while external security services can be integrated where deeper inspection or specialised security controls are required. The objective is not simply to isolate networks, but to make permitted application communication an explicit policy decision.

ACI also provides visibility into the relationships between applications, endpoints, policies, and the underlying network fabric. This can be particularly valuable when troubleshooting complex application environments because network connectivity can be examined in the context of the policies and endpoint relationships that are driving it.

The architecture can support both physical and virtual workloads, making it useful in environments where traditional servers and newer application platforms operate alongside one another. ACI can also participate in hybrid and multi-site designs, although extending policy across different environments requires careful consideration of each site’s networking model, security requirements, and operational boundaries.

Common use cases include data centre consolidation, application segmentation, private cloud infrastructure, highly automated server environments, and organisations managing large numbers of application workloads. In these environments, the value of ACI is less about replacing individual networking technologies and more about creating a consistent operational model for connecting, securing, and automating applications across the data centre.

The most useful way to understand Cisco ACI is therefore as a policy-driven data centre networking architecture. Its combination of fabric networking, application policy, segmentation, automation, and visibility provides a framework for managing increasingly distributed application environments while reducing the dependency on manual, device-by-device network configuration.

Highlights:Cisco ACI | ACI Infrastructure

ACI Fabric Architecture Explorer
Explore how Cisco ACI combines centralized policy, a leaf-spine fabric, endpoint connectivity and distributed forwarding into one application-centric infrastructure.
MANAGEMENT / POLICY APIC Policy • Automation • Fabric Management FABRIC / SPINE Spine 1 High-Speed Fabric Transport Spine 2 High-Speed Fabric Transport ECMP FABRIC PATHS LEAF / VTEP / POLICY EDGE Leaf 1 VTEP • Policy Leaf 2 VTEP • Policy Leaf 3 VTEP • Policy ENDPOINTS Server A Endpoint Server B Endpoint Server C Endpoint DISTRIBUTED FABRIC • CENTRALIZED POLICY
ACI CONCEPT
Application-Centric Infrastructure
Cisco ACI separates the idea of policy from the physical topology. APIC defines the desired policy while the leaf-spine fabric provides distributed connectivity and forwarding.
Key Idea
The leaf is the most important point for understanding ACI from an endpoint perspective. Endpoints attach to leaves, policy is enforced at the edge, and the leaf participates in VXLAN forwarding.
APIC Leaf Spine VTEP ECMP
Click the Fabric
Click any component or connection in the diagram. The panel explains what that element contributes to the ACI architecture.
HOW TO READ THIS ARCHITECTURE
APIC provides centralized policy and automation. Spines provide the high-speed fabric. Leaves connect endpoints and enforce policy. Endpoints consume the application services. Multiple leaf-spine paths provide predictable ECMP connectivity.

The ACI Cisco Architecture

The ACI Cisco operates with several standard ACI building blocks. These include Endpoint Groups (EPGs) that classify and group similar workloads; then, we have the Bridge Domains (BD), VRFs, Contract constructs, COOP protocol in ACI, and micro-segmentation. With micro-segmentation in the ACI, you can get granular policy enforcement right on the workload anywhere in the network.

Unlike in the traditional network design, you don’t need to place certain workloads in specific VLANs or, in some cases, physical locations. The ACI can incorporate devices separate from the ACI, such as a firewall, load balancer, or an IPS/IDS, for additional security mechanisms. This enables the dynamic service insertion of Layer 4 to Layer 7 services. Here, we have a lot of flexibility with the redirect option and service graphs.

1: – The ACI Infrastructure

The Cisco ACI architecture is optimized to learn endpoints dynamically with its dynamic endpoint learning functionality. So, we have endpoint learning in the data plane. Therefore, the other devices learn of the endpoints connected to that local leaf switch; the spines have a mapping database that saves many resources on the spine and can optimize the data traffic forwarding. So you don’t need to flood traffic any more. If you want, you can turn off flooding in the ACI fabric. Then, we have an overlay network.

As you know, the ACI network has both an overlay and a physical underlay; this would be a virtual underlay in the case of Cisco Cloud ACI. The ACI uses VXLAN, the overlay protocol that rides on top of a simple leaf and spine topology, with standards-based protocols such as IS-IS and BGP for route propagation. 

2: – ACI Networks

ACI Networks also introduces the concept of the Application Policy Infrastructure Controller (APIC), which acts as the central point of control for the network. The APIC allows administrators to define and enforce network policies, monitor performance, and troubleshoot issues.

In addition to network virtualization and policy management, ACI Cisco offers a range of other features. These include integrated security, intelligent workload placement, and seamless integration with other Cisco products and technologies.

3: – COOP Protocol in ACI

The spine proxy receives mapping information (location and identity) via the Council of Oracle Protocol (COOP). Using Zero Message Queue (ZMQ), leaf switches forward endpoint address information to spine switches. As part of COOP, the spine nodes maintain a consistent copy of the endpoint address and location information and maintain the distributed hash table (DHT) database for mapping endpoint identity to location.

4: – Micro-segmentation

Integrated security is achieved through micro-segmentation, which allows administrators to define fine-grained security policies at the application level. This helps to prevent the lateral movement of threats within the network and provides better protection against attacks.

Intelligent workload placement ensures that applications are placed in the most appropriate locations within the network based on their specific requirements. This improves application performance and resource utilization.

Cisco ACI Infrastructure — A Policy‑Driven Data Center Fabric

Cisco ACI infrastructure is built on a spine‑leaf architecture that delivers high‑performance, scalable, and policy‑centric networking for modern data centers. Instead of configuring individual switches, ACI abstracts the entire fabric into a unified policy model controlled by the APIC (Application Policy Infrastructure Controller). This creates a programmable, intent‑based environment where application requirements dictate how the network behaves. The infrastructure is designed for automation, segmentation, and consistent forwarding across all nodes, making it ideal for cloud‑ready and microservices‑driven environments.

Spine‑Leaf Architecture — High‑Performance and Predictable Forwarding

ACI uses a spine‑leaf topology, where:

  • Leaf switches connect to endpoints (servers, firewalls, load balancers).
  • Spine switches provide high‑speed interconnects between all leaves.
  • Every leaf connects to every spine, ensuring predictable latency and deterministic paths.

This architecture eliminates oversubscription and unpredictable hop counts found in traditional three‑tier networks. VXLAN overlays provide scalable segmentation, while ACI’s distributed anycast gateway ensures consistent L3 forwarding across all leaf nodes. The result is a fabric that supports east‑west heavy workloads, microservices, and containerized applications with minimal latency.

APIC — Centralized Policy, Automation, and Fabric Control

The Application Policy Infrastructure Controller (APIC) is the brain of the ACI infrastructure. It manages:

  • policy creation and enforcement
  • fabric configuration and automation
  • endpoint learning
  • health scores and telemetry
  • integration with orchestration tools (VMware, Kubernetes, OpenStack)

APIC does not sit in the data path; instead, it provides centralized control while the fabric itself handles forwarding. This separation ensures high availability and simplifies operations. Analytics from APIC and Nexus Dashboard Insights provide deep visibility into fabric health, policy alignment, and anomalies.

Integrated Security and Microsegmentation

ACI infrastructure includes built‑in microsegmentation through Endpoint Groups (EPGs) and contracts. Instead of relying on VLANs and ACLs, segmentation is defined by application intent. The fabric enforces:

  • east‑west isolation
  • workload‑level segmentation
  • identity‑aware policies
  • contract‑based communication rules

Analytics strengthens this by detecting unusual endpoint movement, unexpected contract usage, and abnormal flows between EPGs. This transforms the ACI fabric into a Zero Trust‑aligned data‑center architecture.

VXLAN Overlays and Distributed Routing

ACI uses VXLAN overlays to provide scalable segmentation and multi‑tenant isolation. Each leaf switch acts as a distributed router, providing:

  • consistent L3 gateways
  • scalable tenant isolation
  • efficient east‑west forwarding
  • seamless VM and container mobility

Distributed routing reduces traffic hair‑pinning and improves performance for microservices and cloud‑native workloads. Analytics monitors VXLAN behavior, endpoint mobility, and routing consistency to ensure the fabric remains stable.

Hybrid Cloud Extension — ACI Anywhere

ACI infrastructure extends beyond the physical data center through ACI Anywhere, enabling policy‑driven networking across:

  • AWS
  • Azure
  • Google Cloud
  • Remote data centers
  • Colocation environments

This creates a unified segmentation and security model across hybrid and multi‑cloud architectures. Analytics ensures policy consistency across on‑prem and cloud fabrics, detecting drift, mismatches, and cross‑cloud anomalies.

Data Center Network Challenges

Let us examine well-known data center challenges and how the Cisco ACI network solves them.

Challenge: Traditional Complicated Topology

A traditional data center network design usually uses core distribution access layers. When you add more devices, this topology can be complicated to manage. Cisco ACI uses a simple spine-leaf topology, wherein all the connections within the Cisco ACI fabric are from leaf-to-spine switches, and a mesh topology is between them. There is no leaf-to-leaf and no spine-to-spine connectivity.

Required: How ACI Cisco overcomes this

The Cisco ACI architecture uses the leaf-spine, consisting of a two-tier “fat tree” topology with equidistant bandwidths. The leaf layer connects to the physical and virtual workloads and network services, while the spine layer is the transport layer that interconnects the leaves.

Challenge: Oversubscription

Oversubscription generally means potentially requiring more resources from a device, link, or component than are available. Therefore, the oversubscription ratio must be examined at multiple aggregation points in the design, including the line card to switch fabric bandwidth and the switch fabric input to uplink bandwidth.

Oversubscription Example

Let’s look at a typical 2-layer network topology with access switches and a central core switch. The access switches have 24 user ports and one uplink port connected to the core switch. Each access switch has 24 1Gb user ports and a 10Gb uplink port. So, in theory, if all the user ports are transmitted to a server simultaneously, they would require 24 GB of bandwidth (24 x 1 GB).

However, the uplink port is only 10, limiting the maximum bandwidth to all the user ports. The uplink port is oversubscribed because the theoretical required bandwidth (24Gb) exceeds the available bandwidth (10Gb). Oversubscription is expressed as a ratio of bandwidth needed to available bandwidth. In this case, it’s 24Gb/10Gb or 2.

Challenge: Varying bandwidths

We have layers of oversubscription with the traditional core, distribution, and access designs. We have oversubscribed at the access, distribution, and core layers. The cause of this will give varying bandwidth to endpoints if they want to communicate with an endpoint that is near or an endpoint that is far away. With this approach, endpoints on the same switch will have more bandwidth than two endpoints communicating across the core layer.

Users and application owners don’t care about networks; they want to place their workload wherever the computer is and want the same BW regardless of where you put it. However, with traditional designs, the bandwidth available depends on where the endpoints are located.

Required: How ACI Cisco overcomes this

The ACI leaf and spine have equidistant endpoints between any two endpoints. So if any two servers have the same bandwidths, which is a big plus for data center performance, then it doesn’t matter where you place the workload, which is a big plus for virtualized workloads. This gives you unlimited workload placement.

Challenge: Lack of portability

Applications are built on top of many building blocks. We use contracts such as VLANs, IP addresses, and ACLs to create connectivity. We use these constructs to create and translate the application requirements to the network infrastructure. These constructs are hardened into the network with configurations applied before connectivity.

These configurations are not very portable. It’s not that they were severely designed; they were never meant to be portable. Location Independent Separation Protocol (LISP) did an excellent job making them portable. However, they are hard-coded for a particular requirement at that time. Therefore, if we have the exact condition in a different data center location, we must reconfigure the IP address, VLANs, and ACLs. 

Required: How ACI Cisco overcomes this

An application refers to a set of networking components that provides connectivity for a given set of workloads. These workloads’ relationship is what ACI calls an “application,” and the connection is expressed by what ACI calls an application network profile. With a Cisco ACI design, we can create what is known as Application Network Profiles (ANPs).

The ANP expresses the relationship between the application and its communications. It is a configuration template used to express the relationship between segments. The ACI then translates those relationships into networking constructs such as VLANs, VXLAN, VRF, and IP addresses that the devices in the network can then implement.

Challenge: Issues with ACL

The traditional ACL is very tightly coupled with the network topology, and anything that is tightly coupled will kill agility. It is configured on a specific ingress and egress interface and pre-set to expect a particular traffic flow. These interfaces are usually at demarcation points in the network. However, many other points in the network could do so with security filtering.

Required: How ACI Cisco overcomes this

The fundamental security architecture of the Cisco ACI design follows an allow-list model, where we explicitly define what traffic should be permitted. A contract is a policy construct used to describe communication between EPGs. Without a contract, no unicast communication is possible between those EPGs unless the VRF is configured in “unenforced” mode or those EPGs are in a preferred group.

A contract is not required to communicate between endpoints in the same EPG (although transmission can be prevented with intra-EPG isolation or intra-EPG contract). We have a different construct for applying the policy in ACI. We use the contract construct, and within the contract construct, we have subjects and filters that specify how endpoints are allowed to communicate.

These managed objects are not tied to the network’s topology because they are not applied to a specific interface. Instead, the contracts are used in the intersection between EPGs. They represent rules the network must enforce irrespective of where these endpoints are connected.   

Challenge: Issues with Spanning Tree Protocol (STP)

A significant shortcoming of STP is that it is a brittle failure mode that can bring down entire data centers or campus networks when something goes wrong. Though modifications and enhancements have addressed some of these risks, this has happened at the cost of technical debt in design and maintenance.

When you think about how this works, we have a BPDU that acts as a HELLO mechanism. When we stop receiving the BPDUs and the link stays up, we forward all the links. So, spanning Tree Protocol causes outages.

Spanning Tree Root Switch stp port states

Required: How ACI Cisco overcomes this

The Cisco ACI does not run Spanning Tree Protocol natively, meaning the ACI control plane does not run STP. Inside the fabric, we are running IS-IS as the interior routing protocol. If we stop receiving, we don’t go into an all-forwarding state with IS-IS. As we have IP reachability between Leaf and Spine, we don’t have to block ports and see actual traffic flows that are not the same as the physical topology.

Within the ACI fabric, we have all the advantages of layer three networks, which are more robust and predictable than with an STP design. With ACI, we don’t rely on SPT for the topology design. Instead, the ACI uses ECMP for layer 2 and Layer 3 forwarding. We can use ECMP because we have routed links between the leaves and spines in the ACI fabric.

Challenge: Core-distribution design

The traditional design uses VLANs to segment Layer 2 boundaries and broadcast domains logically. VLANs use network links inefficiently, resulting in rigid device placement. We also have a cap on the number of VLANs we can create. Some applications require that you need Layer 2 adjacencies.

For example, clustering software requires Layer 2 adjacency between source and destination servers. However, if we are routing at the access layer, only servers connected to the same access switch with the same VLANs trunked down would be Layer 2-adjacent. 

Required: How ACI Cisco overcomes this

VXLAN solves this dilemma in ACI by decoupling Layer 2 domains from the underlying Layer 3 network infrastructure. With ACI, we are using the concepts of overlays to provide this abstract. Isolated Layer 2 domains can be connected over a Layer 3 network using VXLAN. Packets are transported across the fabric using Layer 3 routing.

This paradigm fully supports layer 2 networks. Large layer-2 domains will always be needed, for example, for VM mobility, clusters that don’t or can’t use dynamic DNS and non-IP traffic, and broadcast-based intra-subnet communication.

**Cisco ACI Architecture: Leaf and Spine**

The fabric is symmetric with a leaf and spine design, and we have central bandwidth. Therefore, regardless of where a device is connected to the fabric, it has the same bandwidth as every other device connected to the same fabric. This removes the placement restrictions that we have with traditional data center designs. A spine-leaf architecture is a data center network topology that consists of two switching layers—a spine and a leaf.

The leaf layer comprises access switches that aggregate server traffic and connect directly to the spine or network core. Spine switches interconnect all leaf switches in a full-mesh topology.

With low latency east-west traffic, optimized traffic flows are imperative for performance, especially for time-sensitive or data-intensive applications. A spine-leaf architecture aids this by ensuring traffic is always the same number of hops from its next destination, so latency is lower and predictable.

Displaying a VXLAN tunnel 

We have expanded the original design and added VXLAN. We are creating a Layer 2 network, specifically, a Layer 2 overlay over a Layer 3 routed core. The Layer 2 extension allows the hosts, desktop 0 and desktop 1, to communicate over the Layer 2 overlay that VXLAN creates.

The hosts’ IP addresses are 10.0.0.1 and 10.0.0.2, which are not reachable via the Leaf switches. The Leaf switches cannot ping these. Consider the Leaf and Spine switches a standard Layer 3 WAN or network for this lab. So, we have unicast connectivity over the WAN.

The only IP routing addition I have added is the new loopback addresses on Leafs 1 and 2, of 1.1.1.1/32 and 2.2.2.2/32, used for ingress replication for VXLAN. Remember that the ACI is one of many products that use Layer 2 overlays. VXLAN can be used as a Layer 2 DCI.

Notice below I am running a ping from desktop 0 to the corresponding desktop. These hosts are in the 10.0.0.0/8 range, and the core does not know these subnets. I’m also running a packet capture on the link Gi1 connected to Leaf A.

Notice the source and destination are 1.1.1.1 and 2.2.2.2.2, which are the VTEPs. The IMCP traffic is encapsulated into UDP port 1024, explicitly set in the confirmation as the VXLAN port to use.

ACI Network: VXLAN transport network

In a leaf-spine ACI fabric, we have a native Layer 3 IP fabric that supports equal-cost multi-path (ECMP) routing between any two endpoints in the network. Using VXLAN as the overlay protocol allows any workload to exist anywhere in the network.

We can have physical and virtual machines in the same logical layer 2 domain while running layer 3 routing to the top of each rack. Thus, we can connect several endpoints to each leaf, and for one endpoint to communicate with another, we use VXLAN.

So, the transport of the ACI fabric is carried out with VXLAN. The ACI encapsulates traffic with VXLAN and forwards the data traffic across the fabric. Any policy that needs to be implemented gets applied at the leaf layer. All traffic on the fabric is encapsulated with VXLAN. This allows us to support standard bridging and routing semantics without the standard location constraints.

Diagram: VXLAN operations. The source is Cisco.

Council of Oracle Protocol

COOP protocol in ACI and the ACI fabric

The fabric appears to the outside as one switch capable of forwarding Layers 2 and 3. In addition, the fabric is a Layer 3 network routed network and enables all links to be active, providing ECMP forwarding in the fabric for both Layer 2 and Layer 3. Inside the fabric, we have routing protocols such as BGP; we also use Intermediate System-to-Intermediate System Protocol (IS-IS) and Council of Oracle Protocol (COOP) for all forwarding endpoint-to-endpoint communications.

The COOP protocol in ACI communicates the mapping information (location and identity) to the spine proxy. A leaf switch forwards endpoint address information to the spine switch ‘Oracle’ using Zero Message Queue (ZMQ). The COOP protocol in ACI is something new to data centers. The Leaf switches use COOP to report local station information to the Spine (Oracle) switches.

COOP protocol in ACI

Let’s look at an example of how the COOP protocol in ACI works. We have a Leaf that learns of a host. The Leaf reports this information—let’s say it knows Host B—and sends it to one of the Spine switches chosen randomly using the Council Of Oracle Protocol.

The Spine switch then relays this information to all the other Spines in the ACI fabric so that every Spine has a complete record of every single endpoint. The Spines switches record the information learned via the COOP in the Global Proxy Table, which resolves unknown destination MAC/IP addresses when traffic is sent to the Proxy address.

COOP database.

So, we know that the Spine has a COOP database of all endpoints in the fabric. Council of Oracle Protocol (COOP) is used to communicate the mapping information (location and identity) to the spine proxy. A leaf switch forwards endpoint address information to the spine switch ‘Oracle’ using Zero Message Queue (ZMQ).

The command: Show coop internal info repo key allows us to verify that the endpoint is in the COOP database using the BD VNID of 16154554 mapped to the MAC address of 0050.5690.3eeb. With this command, you can also see the tunnel next hop and IPv4 and IPv6 addresses tied to this MAC address.

coop protocol in ACI
Diagram: COOP protocol in ACI

**The fabric constructs**

The ACI Fabric contains several new network constructs specific to ACI that enable us to abstract much of the complexity we had with traditional data center designs. These new concepts are ACI’s Endpoint Groups, Contracts, Bridge Domains, and COOP protocol.

In addition, we have a distributed Layer 3 Anycast gateway function that ensures optimal Layer 3 and Layer 2 forwarding. We also have original constructs you may have used, such as VRFs. The Layer 3 Anycast feature is popular and allows flexible placement of the default gateway, which is suited for agile designs.

 

Cisco ACI • Interactive Learning Widget 2

ACI Traffic Flow & VXLAN Explorer

Follow one packet from the source endpoint through the ACI fabric to the destination endpoint.
ACI PACKET JOURNEY SOURCE FABRIC TRANSPORT DESTINATION Server A Source Endpoint Traffic enters fabric Leaf 1 Source Fabric Edge VTEP 1 VXLAN Encapsulation Policy / Forwarding Edge Spine Fabric Physical Underlay ECMP Multiple Fabric Paths High-Speed Transport No Endpoint Attachment Leaf 2 Destination Fabric Edge VTEP 2 VXLAN Decapsulation Destination Processing Server B Destination Endpoint Final delivery ACCESS UNDERLAY ECMP ACCESS VXLAN OVERLAY • VTEP 1 → VTEP 2 VXLAN PACKET JOURNEY Server A → Leaf 1 → Spine Fabric → Leaf 2 VXLAN overlay runs between VTEP 1 and VTEP 2 Leaf 2 → Server B
PACKET JOURNEY
Complete Packet Journey
A packet enters through the source leaf, is carried across the physical spine fabric, and reaches the destination leaf. The VXLAN overlay provides the logical tunnel between the source and destination VTEPs.
Key Idea VXLAN is the overlay. The spine-leaf fabric is the physical transport underneath it.
Leaf VXLAN VTEP ECMP
What to Watch
  • Server A enters Leaf 1.
  • VTEP 1 starts the VXLAN overlay.
  • The spine provides physical transport.
  • VTEP 2 terminates the overlay.
  • Leaf 2 delivers traffic to Server B.

Source Leaf

Leaf 1 is the endpoint-facing edge. It receives traffic from Server A and provides the source-side VTEP function.

VXLAN / VTEP

VTEP 1 and VTEP 2 define the tunnel endpoints. VXLAN provides the logical overlay across the fabric.

Spine Fabric

The spine provides physical transport between leaves. ECMP gives the fabric multiple forwarding paths.

Cisco ACI | ACI Infrastructure

ACI Infrastructure

Key components that make up the ACI Cisco architecture. By understanding these components, network administrators and IT professionals can harness the power of ACI to optimize their data center operations.

1. Application Policy Infrastructure Controller (APIC):

The cornerstone of the Cisco ACI architecture is the Application Policy Infrastructure Controller (APIC). APIC is the central management and policy engine for the entire ACI fabric. It provides a single point of control, enabling administrators to define and enforce policies that govern the behavior of applications and services within the data center. APIC offers a user-friendly interface for policy configuration, monitoring, and troubleshooting, making it an essential component for managing the ACI fabric.

2. Spine Switches:

Spine switches form the backbone of the ACI fabric. These high-performance switches provide connectivity between leaf switches and facilitate east-west traffic within the fabric. Spine switches operate at Layer 3 and use routing protocols to efficiently distribute traffic across the fabric. With the ability to handle massive amounts of data, spine switches ensure high-speed connectivity and optimal performance in the ACI environment.

3. Leaf Switches:

Leaf switches act as the access layer switches in the ACI fabric. They connect directly to the endpoints, such as servers, storage devices, and other network devices, and serve as the entry and exit points for traffic entering and leaving the fabric. Leaf switches provide Layer 2 connectivity for endpoint devices and Layer 3 connectivity for communication between endpoints within the fabric. They also play a crucial role in implementing policy enforcement and forwarding traffic based on predefined policies.

**Example: Cisco ACI & IS-IS**

Cisco ACI under the covers runs ISIS. The ISIS routing protocol is an Interior Gateway Protocol (IGP) that enables routers within a network to exchange routing information and make informed decisions on the best path to forward packets. It operates at the OSI model’s Layer 2 (Data Link Layer) and Layer 3 (Network Layer).

ISIS organizes routers into logical groups called areas, simplifying network management and improving scalability. It allows for hierarchical routing, reducing the overhead of exchanging routing information across large networks.

**Note: IS-IS Parameters**

Below, we have four routers. R1 and R2 are in area 12, and R3 and R4 are in area 34. R1 and R3 are intra-area routers so that they will be configured as level 1 routers. R2 and R4 form the backbone so that these routers will be configured as levels 1-2.

Network administrators need to configure ISIS parameters on each participating router to implement ISIS. These parameters include the router’s ISIS system ID, area assignments, and interface settings. ISIS uses the reliable transport protocol (RTP) to exchange routing information between routers.

Routing Protocol
Diagram: Routing Protocol. ISIS.

4. Application Network Profiles (ANPs):

Application Network Profiles (ANPs) are a key Cisco ACI policy model component. They define the policies and configurations required for specific applications or application groups and encapsulate all the necessary information, including network connectivity, quality of service (QoS) requirements, security policies, and service chaining.

By associating endpoints with ANPs, administrators can easily manage and enforce consistent policies across the ACI fabric, simplifying application deployment and ensuring compliance.

5. Endpoint Groups (EPGs):

Endpoint Groups (EPGs) are logical containers that group endpoints with similar network requirements. EPGs provide a way to define and enforce policies at a granular level—endpoints within an EPG share standard policies, such as security, QoS, and network connectivity.

This grouping allows administrators to apply policies consistently to specific endpoints, regardless of their physical location within the fabric. EPGs enable seamless application mobility and simplify policy enforcement within the ACI environment.

**Specific ACI Cisco architecture**

In some of the lab guides in this blog post, we are using the following hardware from a rack rental from Cloudmylabs. Remember that the ACI Fabric is built on the Nexus 9000 Product Family.

The Cisco Nexus 9000 Series Switches are designed to meet the increasing demands of modern networks. With high-performance capabilities, these switches deliver exceptional speeds and low latency, ensuring smooth and uninterrupted data flow. They support high-density 10/25/40/100 Gigabit Ethernet interfaces, allowing businesses to scale and adapt to growing network requirements.

Enhanced Security:

The Cisco Nexus 9000 Series Switches offer comprehensive security features to protect networks from evolving threats. They leverage Cisco TrustSec technology, which provides secure access control, segmentation, and policy enforcement. With integrated security features, businesses can mitigate risks and safeguard critical data, ensuring peace of mind.

Application Performance Optimization:

To meet the demands of modern applications, the Cisco Nexus 9000 Series Switches are equipped with advanced features that optimize application performance. These switches support Cisco Tetration Analytics, which provides deep insights into application behavior, enabling businesses to enhance performance, troubleshoot issues, and improve efficiency.

Diagram: The source is Cloudmylabs.

Cisco ACI Simulator:

Below is a screenshot from Cisco ACI similar to the one below. At the start, you will be asked for the details of the fabric. Remember that once you set the out-of-band management address for the API, you need to change the port group settings on the ESXi VM network. If you don’t change “Promiscuous mode, MAC address changes, and Forged Transmits,” you cannot access the UI from your desktop.

ACI fabric Details
Diagram: Cisco ACI fabric Details

Leaf and spine design

Network Design Methodology

Leaf and spine architecture is a network design methodology commonly used in data centers. It provides a scalable and resilient infrastructure that can handle the increasing demands of modern applications and services. The term “leaf and spine” refers to the physical and logical structure of the network.

In leaf and spine architecture, the network is divided into two main layers: the leaf and spine layers. The leaf layer consists of leaf switches connected to the servers or endpoints in the data center. These leaf switches act as the access points for the servers, providing high-bandwidth connectivity and low-latency communication.

The spine layer, on the other hand, consists of spine switches that connect the leaf switches. The spine switches provide high-speed and non-blocking interconnectivity between the leaf switches, forming a fully connected fabric. This allows for efficient and predictable traffic patterns, as any leaf switch can communicate directly with any other leaf switch through the spine layer.

ACI Cisco with leaf and spine.

The following lab guide has a leaf and spine ACI design that includes 2 leaf switches acting as the leaf layer where the workloads connect. Then, we have a spine attached to the leaf. When the ACI hardware installation is done, all Spines and Leafs are linked and powered up. Once the basic configuration of APIC is completed, the Fabric discovery process starts working.

Note: IFM process

In the discovery process, ACI uses the Intra-Fabric Messaging (IFM) process in which APIC and nodes exchange heartbeat messages.

The process used by the APIC to push policy to the fabric leaf nodes is called the IFM Process. ACI Fabric discovery is completed in three stages. The leaf node directly connected to the APIC is discovered in the first stage. The second discovery stage brings in the spines connected to that initial leaf where APIC was connected. The third stage involves discovering the cluster’s other leaf nodes and APICs.

The fabric membership diagram below shows the inventory, including serial number, Pod, Node ID, Model, Role, Fabric IP, and Status. Cisco ACI consists of the following hardware components: APIC Controller Spine Switches and Leaf Switches.

ACI fabric discovery
Diagram: ACI fabric discovery

Analysis: Overlay based on VXLAN

Cisco ACI uses an overlay based on VXLAN to virtualize physical infrastructure. Like most overlays, this overlay requires the data path at the network’s edge to map from the tenant end-point address in the packet, otherwise referred to as its identifier, to the endpoint’s location, also known as its locator. This mapping occurs in a tunnel endpoint (TEP) function called VXLAN (VTEP).

VTEP Addressing

The VTEP addresses are displayed in the INFRASTRUCTURE IP column. The TEP address pool 10.0.0.0/16 has been configured on the Cisco APIC using the initial setup dialog. The APIC assigns the TEP addresses to the fabric switches via DHCP, so the infrastructure IP addresses in your fabric will differ from the figure.

This configuration is perfectly valid for a Lab but not good for a production environment. The minimum physical fabric hardware for a production environment includes two spines, two leaves, and three APICs.In addition to discovering and configuring the Fabric and applying the Tenant design, the following functionality can be configured:

  1. Routing at Layer 3
  2. Connecting a legacy network at layer 2
  3. Virtual Port Channels at Layer 2

Note: Border Leaf

A note about Border Leafs: ACI fabrics often use this designation along with “Compute Leafs” and “Storage Leafs.” Border Leaf is merely a convention for identifying the leaf pair that hosts all external connectivity external to the fabric (Border Leaf) or the leaf pair that hosts host connectivity (Compute Leaf).

Note: The Link Layer Discovery Protocol (LLDP)

LLDP is responsible for discovering directly adjacent neighbors. When run between the Cisco APIC and a leaf switch, it precedes three other processes: Tunnel endpoint (TEP) IP address assignment, node software upgrade (if necessary), and the intra-fabric messaging (IFM) process, which the Cisco APIC uses to push policy to the leaves.

aci Cisco LLDP

Leaf and Spine: Traffic flows

The leaf and spine network topology is suitable for east-to-west network traffic and comprises leaf switches to which the workloads connect and spine switches to which the leaf switches connect. The spines have a simple role and are geared around performance, while all the intelligence is distributed to the edge of the network, where the leaf layers sit.

This allows engineers to move away from managing individual devices and more efficiently manage the data center architecture with policy. In this model, the Application Policy Infrastructure Controller (APIC) controllers can correlate information from the entire fabric.

**Understanding Leaf and Spine Traffic Flow**

In a leaf and spine architecture, traffic flow follows a structured path. When a device connected to a leaf switch wants to communicate with another device, the traffic is routed through the spine switch to the destination leaf switch. This approach minimizes the hops required for data transmission and reduces latency. Additionally, traffic can be evenly distributed since every leaf switch is connected to every spine switch, preventing congestion and bottlenecks.

**ACI Cisco with leaf and spine**

In the following lab guide, we continue to verify the ACI leaf and spine. To check the ACI fabric, we can run the diagnostic tool Acidiag fnvread. It is also recommended that the LLDP and ISIS adjacencies be checked. With a leaf and spine design, the leaf layer does not connect, and we can see this with the LLDP and ISIS adjacency information below.

ACI leaf and spine
Diagram: ACI leaf and spine

Leaf and Spine Switch Functions

Based on a two-tier (spine and leaf switches) or three-tier (spine switch, tier-1 leaf switch, and tier-2 leaf switch) architecture, Cisco ACI switches provide the following functions:

What are Leaf Switches?

Leaf switches connect between end devices, servers, and the network fabric. They are typically deployed in leaf-spine network architecture, connecting directly to the spine switches. Leaf switches provide high-speed, low-latency connectivity to end devices within a data center network.

Functionalities of Leaf Switches:

1. Aggregation: Leaf switches aggregate traffic from multiple servers and sends it to the spine switches for further distribution. This aggregation helps reduce the network’s complexity and enables efficient traffic flow.

2. High-density Port Connectivity: Leaf switches are designed to provide a high-density port connectivity environment, allowing multiple devices to connect simultaneously. This is crucial in data centers where numerous servers and devices must be interconnected.

These devices have ports connected to classic Ethernet devices, such as servers, firewalls, and routers. In addition, these leaf switches provide the VXLAN Tunnel Endpoint (VTEP) function at the edge of the fabric. In Cisco ACI terminology, IP addresses representing leaf switch VTEPs are called Physical Tunnel Endpoints (PTEPs). The leaf switches route or bridge tenant packets and applies network policies.

What are Spine Switches?

Spine switches, also known as spine or core switches, are high-performance switches that form the backbone of a network. They play a vital role in data centers and large enterprise networks and facilitate the seamless data flow between various leaf switches.

These devices interconnect leaf switches and can also connect Cisco ACI pods to IP networks or WAN devices to build a Cisco ACI Multi-Pod fabric. In addition to mapping entries between endpoints and VTEPs, spine switches also store proxy entries between endpoints and VTEPs. Leaf switches are connected to spine switches within a pod, and spine switches are connected to leaf switches.

No direct connection between tier-1 leaf switches, tier-2 leaf switches, or spine switches is allowed. If you incorrectly cable spine switches to each other or leaf switches in the same tier to each other, the interfaces will be disabled.

Cisco ACI Fabric
Diagram: Cisco ACI Fabric. Source Cisco Live.

BGP Route Reflection

Under the cover, Cisco ACI works with BGP Route-Reflection. BGP Route Reflection creates a hierarchy of routers within the ACI fabric. At the top of the hierarchy is a Route-Reflector (RR), a central point for collecting routing information from other routers within the fabric. The RR then reflects this information to other routers, ensuring that every router in the network has a complete view of the routing table.

The ACI uses MP-BGP protocol to distribute external Network subnets or prefixes inside the ACI fabric. To create an MP-BGP route reflector, we must select two Spines acting as Route Reflectors and make an iBGP Neighbourship to all other leaves.

ACI Cisco and endpoints

In a traditional network, three tables are used to maintain the network addresses of external devices: a MAC address table for Layer 2 forwarding, a Routing Information Base (RIB) for Layer 3 forwarding, and an ARP table for the combination of IP addresses and MAC addresses. Cisco ACI, however, maintains this information differently, as shown below.

ACI Endpoint learning
Diagram: Endpoint Learning. Source Cisco.com

What is ACI Endpoint Learning?

ACI endpoint learning refers to discovering and monitoring the network endpoints within an ACI fabric. Endpoints include devices, virtual machines, physical servers, users, and applications. Network administrators can make informed decisions regarding network policies, security, and traffic optimization by gaining insights into these endpoints’ location, characteristics, and behavior.

How Does ACI Endpoint Learning Work?

ACI fabric leverages a distributed, controller-based architecture to facilitate endpoint learning. When an endpoint is connected to the fabric, ACI utilizes various mechanisms to gather information about it. These mechanisms include Address Resolution Protocol (ARP) snooping, Link Layer Discovery Protocol (LLDP), and even integration with hypervisor-based systems.

Once an endpoint is detected, ACI Fabric builds a comprehensive endpoint database called the Endpoint Group (EPG). This database contains vital information such as MAC addresses, IP addresses, VLANs, and associated policies. By continuously monitoring and updating this database, ACI ensures real-time visibility and control over the network endpoints.

Implementation Endpoint Learning Considerations:

To leverage the benefits of ACI endpoint learning, organizations need to consider a few key aspects:

1. Infrastructure Design: A well-designed ACI fabric with appropriate leaf and spine switches is crucial for efficient endpoint learning. Proper VLAN and subnet design should be implemented to ensure accurate endpoint identification and classification.

2. Endpoint Group (EPG) Definition: Defining and associating EPGs with appropriate policies is essential. EPGs help categorize endpoints based on their characteristics, allowing for granular policy enforcement and simplified management.

Diagram: ACI Endpoint Learning. The source is Cisco.

Forwarding Behavior. The COOP database

Local and remote endpoints are learned from the data plane, but remote endpoints are local caches. Cisco ACI’s fabric relies heavily on local endpoints for endpoint information. A leaf is responsible for reporting its local endpoints to the Council Of Oracle Protocol (COOP) database located on each spine switch, which implies that all endpoint information in the Cisco ACI fabric is stored there.

Each leaf does not need to know about all the remote endpoints to forward packets to the remote endpoints because this database is accessible. When a leaf does not know about a remote endpoint, it can still forward packets to spine switches. This forwarding behavior is called spine proxy.

Diagram: Endpoint Learning. The source is Cisco.

In a traditional network environment, switches rely on the Address Resolution Protocol (ARP) to map IP addresses to MAC addresses. However, this approach becomes inefficient as the network scales, resulting in increased network traffic and complexity. Cisco ACI addresses this challenge by utilizing local endpoint learning, a more intelligent and efficient method of mapping MAC addresses to IP addresses.

Diagram: Local and Remote endpoint learning. The source is Cisco.

ACI Cisco: The Main Features

We are experiencing many changes right now that are impacting almost every aspect of IT. Applications are changing immensely, and we see their life cycles broken into smaller windows as they become less structured. In addition, containers and microservices are putting new requirements on the underlying infrastructure, such as the data centers they live in. This is one of the main reasons why a distributed system, including a data center, is better suited for this environment.

Distributed system/Intelligence at the edge

Like all networks, the Cisco ACI network still has a control and data plane. From the control and data plane perspective, the Cisco ACI architecture is still a distributed system. Each switch has intelligence and knows what it needs to do—one of the differences between ACI and traditional SDN approaches that try to centralize the control plane. If you try to centralize the control plan, you may hit scalability limits, not to mention a single point of failure and an avenue for bad actors to penetrate.

Cisco ACI Design
Diagram: Cisco ACI Design. Source Cisco Live.

Two large core devices

If we examine the traditional data center architecture, intelligence is often in two central devices. You could have two large core devices. What the network used to control and secure has changed dramatically with virtualization via hypervisors. We’re seeing faster change with containers and microservices being deployed more readily.

As a result, an overlay networking model is better suited. However, in a VXLAN overlay network, the intelligence is distributed across the leaf switch layer.

Therefore, distributed systems are better than centralized systems for more scale, resilience, and security. By distributing intelligence to the leaf layer, scalability is not determined by the scalability of each leaf but by the fabric level. However, there are scale limits on each device. Therefore, scalability as a whole is determined by the network design.

A key point: Overlay networking

The Cisco ACI architecture provides an integrated Layer 2 and 3 VXLAN-based overlay networking capability to offload network encapsulation processing from the compute nodes onto the top-of-rack or ACI leaf switches. This architecture provides the flexibility of software overlay networking in conjunction with the performance and operational benefits of hardware-based networking. 

ACI Cisco New Concepts

Networking in the Cisco ACI architecture differs from what you may use in traditional network designs. It’s not different because we use an entirely new set of protocols. ACI uses standards-based protocols such as BGP, VXLAN, and IS-IS. However, the new networking constructs inside the ACI fabric exist only to support policy.

ACI has been referred to as stateless architecture. As a result, the network devices have no application-specific configuration until a policy is defined stating how that application or traffic should be treated on the network.

This is a new and essential concept to grasp. So, now, with the ACI, the network devices in the fabric have no application-specific configuration until there is a defined policy. No configuration is tied to a device. With a traditional configuration model, we have many designs on a device, even if it’s not being used. For example, we had ACL and QoS parameters configured, but nothing was using them.

The APIC controller

The APICs, the management plan that defined the policy, do not need to push resources when nothing connected utilizes that. The APIC controller can see the entire fabric and has a holistic viewpoint.

Therefore, it can correlate configurations and integrate them with devices to help manage and maintain the security policy you define. We see every device on the fabric, physical or virtual, and can maintain policy consistency and, more importantly, recognize when policy needs to be enforced. 

APIC Controller
Diagram: APIC Controller. Source Cisco Live.

Endpoint groups (EPG)

We touched on this a moment ago. Groups or endpoint groups (EPGs) and contracts are core to the ACI. Because this is a zero-trust network by default, communication is blocked in hardware until a policy consisting of groups and contracts is defined. With Endpoint Groups, we can decouple and separate the physical or virtual workloads from the constraints of IP addresses and VLANs. 

So, we are grouping similar workloads into groups known as Endpoint Groups. Then, we can control group behavior by applying policy to the groups and not the endpoints in the group. As a security best practice, it is essential to group similar workloads with similar security sensitivity levels and then apply the policy to the endpoint group.

For example, a traditional data center network could have database and application servers in the same segment controlled by a VLAN with no intra-VLAN filtering. The EPG approach removes the barriers we have had with traditional networks, with the limitation of the IP address being used as the identifier and locator and the restrictions of the VLANs.

This is a new way of thinking, and it allows devices to communicate with each other without changing their IP address, VLAN, or subnet.

ACI Endpoint Groups
Diagram: ACI Endpoint Groups. Source Cisco Live.

EPG Communication

The EPG provides a better segmentation method than the VLAN, which was never meant to live in a world of security. By default, anything in the group can communicate freely, and inter-EPG communication needs a policy. This policy construct that ACI uses is called a contract. So, having similar workloads of similar security levels in the same EPG makes sense. All devices inside the same endpoint group can talk to each other freely.

This behavior can be modified with intra-EPG isolation, similar to a private VLAN where communication between group members is not allowed. Or, intra-EPG contracts can be used only to allow specific communications between devices in an EPG.

Extending the ACI Fabric

Developing the Cisco ACI architecture

I have always found extending data risky when undergoing data center network design projects. However, the Cisco ACI architecture can be extended without the traditional Layer 2 and 3 Data Center Interconnect (DCI) mechanisms. Here, we can use Multi-Pod and Multi-Site to better control large environments that need to span multiple locations and for applications to share those multiple locations in active-active application deployments.

Diagram: Extending the ACI fabric. Source is Cisco

When considering data center designs, terms such as active-active and active-passive are often discussed. In addition, enterprises are generally looking for data center solutions that provide geographical redundancy for their applications.

Enterprises also need to be able to place workloads in any data center where computing capacity exists—and they often need to distribute members of the same cluster across multiple data center locations to provide continuous availability in the event of a data center failure. The ACI gives us options for extending the fabric to multiple locations and location types.

For example, there are stretched fabric, multi-pod, multi-site designs, and, more recently, Cisco Cloud ACI.

ACI design: Multi pod

The ACI Multi-Pod is the next evolution of the original stretch fabric design we discussed. The architecture consists of multiple ACI Pods connected by an IP Inter-Pod Layer 3 network. With the stretched fabric, we have one Pod across several locations. Cisco ACI MultiPod is part of the “single APIC cluster/single domain” family of solutions; a single APIC cluster is deployed to manage all the interconnected ACI networks.

These ACI networks are called “pods,” Each looks like a regular two-tier spine-leaf topology. The same APIC cluster can manage several pods.  All of the nodes deployed across the individual pods are under the control of the same APIC cluster. The separate pods are managed as if they were logically a single entity. This gives you operational simplicity. We also have a fault-tolerant fabric since each Pod has isolated control plane protocols.

Diagram: Multi-pod. Source is Cisco

ACI design: Cisco cloud ACI

Cisco Cloud APIC is an essential new solution component introduced in the architecture of Cisco Cloud ACI. It plays the equivalent of APIC for a cloud site. Like the APIC for on-premises Cisco ACI sites, Cloud APIC manages network policies for the cloud site it runs on by using the Cisco ACI network policy model to describe the policy intent.

ACI design: Multisite

ACI Multi-Site enables you to interconnect separate APIC cluster domains or fabric, each representing a separate availability zone. As a result, we have separate and independent APIC domains and fabrics. This way, we can manage multiple fabrics as regions or availability zones. ACI Multi-Site is the easiest DCI solution in the industry. Communication between endpoints in separate sites (Layers 2 and 3) is enabled simply by creating and pushing a contract between the endpoints’ EPGs.

Cisco ACI • Widget 3

Endpoint Learning & COOP Explorer

Follow how an ACI leaf learns an endpoint locally, how the endpoint is distributed through COOP, and how another leaf can resolve the remote endpoint through the spine.
ENDPOINT Server A 10.10.10.10 MAC AA:AA:AA LEAF EDGE Leaf 1 Local endpoint learning Local Endpoint MAC → EPG / interface COOP Endpoint DB Control-plane endpoint knowledge 10.10.10.10 → Leaf 1 SPINE Spine Proxy COOP lookup remote endpoint resolution REMOTE LEAF Leaf 2 Destination-side edge knows endpoint location and forwarding context REMOTE ENDPOINT Server B 10.20.20.20 MAC BB:BB:BB 1 • LEARN Leaf records local endpoint 2 • REGISTER Endpoint information enters COOP 3 • DISTRIBUTE Fabric shares endpoint knowledge 4 • RESOLVE Remote leaf can resolve destination WHY COOP MATTERS ACI does not require every leaf to independently flood the entire endpoint database. COOP provides a fabric-wide control-plane view used for endpoint resolution.
ACI CONCEPT

Endpoint Learning

An ACI leaf learns endpoints at the edge of the fabric. The leaf can associate an endpoint with information such as its MAC/IP, interface, EPG and local forwarding context.

Key Idea Endpoint learning happens close to the endpoint, while COOP helps distribute endpoint knowledge across the fabric.
Endpoint Learning COOP Spine Proxy Remote Leaf Forwarding
Local Leaf = First Knowledge The leaf directly attached to an endpoint learns where that endpoint exists and builds the local forwarding context.
COOP = Fabric Endpoint Knowledge COOP provides the control-plane mechanism used to maintain and resolve endpoint information across the ACI fabric.
Spine = Proxy for Resolution When a leaf needs information about an endpoint elsewhere in the fabric, the spine can provide the control-plane lookup function.
rsz_secure_access_service_edge1

SASE Definition

As organisations distribute applications, users, and workloads across data centres, public cloud platforms, SaaS services, branches, and remote locations, the traditional model of sending traffic back through a central corporate network becomes increasingly difficult to scale. Secure Access Service Edge (SASE) addresses this challenge by bringing networking and security capabilities together through a cloud-delivered architectural model designed for distributed environments.

SASE combines software-defined networking with security services so that connectivity and security policy can be applied closer to users, devices, branches, and applications. Rather than treating the corporate data centre as the primary security boundary, SASE recognises that users and applications may exist in many different locations. The architecture therefore focuses on providing secure, policy-controlled access regardless of where the connection originates.

Several technologies commonly contribute to a SASE architecture. SD-WAN provides software-defined connectivity and traffic steering across transports such as MPLS, broadband, Internet, and cellular networks. Security services can include Secure Web Gateway (SWG), Firewall as a Service (FWaaS), Cloud Access Security Broker (CASB), Zero Trust Network Access (ZTNA), and data protection capabilities. The exact combination varies between implementations and providers, but the architectural objective is to bring these capabilities together through a consistent policy and operational model.

Zero Trust is particularly important to the SASE approach. Access decisions should not be based simply on whether a user is connected to a trusted corporate network. Instead, policies can consider identity, device posture, application, location, security context, and the sensitivity of the requested resource. Authentication and authorisation therefore become part of the access decision rather than an assumption made from network location alone.

SASE can also simplify operations by reducing the need to deploy and manage separate security appliances at every branch or user location. Cloud-delivered services can provide centrally managed policy while allowing enforcement to occur closer to the user or application. This can improve consistency, but implementation still requires careful planning around identity, existing infrastructure, application dependencies, traffic flows, security policy, and operational processes.

Performance is another important consideration. Distributed users accessing cloud and SaaS applications may not benefit from routing all traffic through a central data centre. SASE architectures can combine SD-WAN traffic steering with distributed security enforcement to select appropriate paths while maintaining security policy. Network performance, application experience, latency, packet loss, and service availability therefore become important operational measurements alongside traditional security events.

SASE is particularly relevant to hybrid and multi-cloud environments because the network boundary is no longer defined by a single physical location. Organisations may need to connect offices, remote users, private data centres, public clouds, SaaS applications, and third-party services while maintaining consistent access controls and security policies across these environments.

The most useful way to understand SASE is not as a single product or technology, but as an architectural approach for converging networking and security around distributed users and applications. Its effectiveness depends on how well connectivity, identity, security policy, application access, visibility, and operations are integrated. For organisations modernising their WAN and security architecture, SASE provides a framework for building secure access around the way applications and users actually operate today.

SASE Definition

Secure Access Service Edge • Widget 1

SASE Architecture Explorer

See how SASE brings networking and security together as a cloud-delivered service, with enforcement distributed closer to users, branches and applications.
USER / DEVICE Remote User Laptop / Mobile Anywhere EDGE Branch / Site Office / Campus SD-WAN edge SASE EDGE SASE PoP Distributed point of presence CONNECTIVITY SD-WAN / routing SECURITY inspection / control POLICY consistent enforcement CLOUD FABRIC SASE Cloud Networking + Security SD-WAN path control SWG web security CASB cloud control ZTNA access control FWaaS cloud firewall CLOUD APP SaaS Microsoft 365 CRM / Web Apps PRIVATE APP Enterprise Data Center / Private Cloud CORE SASE IDEA Connect users and sites to distributed SASE edges, then apply networking and security services close to the access point. CONVERGED SERVICES One cloud-delivered architecture
SASE CONCEPT

SASE Overview

SASE combines networking and security capabilities into a cloud-delivered architecture. Instead of forcing users and sites through a centralized security perimeter, services can be delivered from distributed points of presence.

Key Idea SASE is not simply a collection of security products. The important architectural change is the convergence of networking, security, cloud delivery and distributed enforcement.
SD-WAN SWG CASB ZTNA FWaaS
Cloud Delivered Networking and security functions are delivered from a distributed cloud architecture rather than requiring a separate appliance at every location.
Distributed Enforcement Users and sites can connect to nearby SASE PoPs, reducing dependence on traditional centralized security choke points.
Unified Architecture Routing, security and access policies can be managed as part of one operational model instead of many disconnected point solutions.

Highlights: SASE Definition

SASE: A Cloud-Centric Approach

Firstly, the SASE is related to our environment. In a cloud-centric world, users and devices require access to services everywhere. The focal point has changed. Now, the identity of the user and device, as opposed to the traditional model, focuses solely on the data center with many network security components. These environmental changes have created a new landscape we must protect and connect.

Many common problems challenge the new landscape. Due to deployed appliances for different technology stacks, enterprises are loaded with complexity and overhead. The legacy network and security designs increase latency. In addition, the world is encrypted when considering Zero Trust SASE. This needs to be inspected without degrading application performance.

These are reasons to leverage a cloud-delivered secure access service edge (SASE). SASE means a tailored network fabric optimized where it makes the most sense for the user, device, and application – at geographically dispersed PoPs enabling technologies that secure your environment with technologies such as single packet authorization.

**Driving Forces to Adopting SASE**

Challenge 1: Managing the Network: Converging network and security into a single platform does not require multiple integration points. This will eliminate the need to deploy these point solutions and the complexities of managing each.

Challenge 2: Site Connectivity: SASE handles all management complexities. As a result, the administrative overhead for managing and operating a global network that supports site-to-site connectivity and enhanced security, cloud, and mobility is kept to an absolute minimum.

Challenge 3: Performance Between Locations: The SASE cloud already has an optimized converged network and security platforms. Therefore, sites need to connect to the nearest SASE PoP.

Challenge 4: Cloud Agility: SASE natively supports cloud data centers (IaaS) and applications (SaaS) without additional configuration, complexity, or point solutions, enabling built-in cloud connectivity.

SASE Definition — A Converged Cloud Architecture for Secure Connectivity

Secure Access Service Edge (SASE) is a cloud‑delivered architecture that merges networking and security into a single, unified service model. Instead of deploying separate appliances for WAN connectivity, web filtering, cloud security, and remote access, SASE consolidates these capabilities into a distributed cloud fabric. The goal is simple: provide consistent, high‑performance, secure access to applications—no matter where users are located or where workloads reside. SASE shifts security and networking away from traditional data‑center choke points and distributes them across global points of presence, creating a seamless edge‑to‑cloud access layer.

Convergence of Networking and Security Services

SASE is defined by the convergence of two historically separate domains:

  • Network services such as SD‑WAN, traffic optimization, and dynamic path selection
  • Security services such as SWG, CASB, FWaaS, and threat inspection

By delivering these as cloud‑native functions, SASE eliminates the complexity of managing multiple point products. Policies, routing decisions, and security controls are applied consistently across all edges—branch, remote, mobile, and cloud. This convergence ensures that performance and protection are no longer competing priorities but integrated outcomes of the same architecture.

Cloud‑Native Enforcement at Global Points of Presence

A defining characteristic of SASE is its distributed enforcement model. Instead of backhauling traffic to a central firewall or VPN concentrator, SASE evaluates and secures traffic at cloud points of presence (PoPs) located close to users and applications. These PoPs provide:

  • low‑latency inspection
  • scalable policy enforcement
  • optimized routing to SaaS and cloud platforms
  • unified visibility across all user traffic

This distributed model supports hybrid work, multi‑cloud adoption, and global operations without relying on legacy hub‑and‑spoke designs.

Unified Policy, Visibility, and Operational Control

SASE centralizes policy management into a single cloud platform. Administrators define access rules, security controls, and routing preferences once, and the SASE fabric enforces them everywhere. This unified control plane provides:

  • consistent policy enforcement
  • real‑time visibility into user and application behavior
  • simplified operations across distributed environments
  • automated updates and continuous improvement

SASE reduces operational overhead by replacing manual configuration with cloud‑driven orchestration and analytics‑based optimization.

Outcome‑Driven Architecture for Modern Enterprises

SASE is not just a technology stack—it is an operational model designed to deliver measurable outcomes:

  • Improved performance through optimized cloud and SaaS access
  • Reduced complexity by consolidating multiple security and networking tools
  • Stronger protection through unified inspection and consistent policy enforcement
  • Scalability for global users, remote workforces, and cloud‑native applications

By merging connectivity and security into a single cloud‑delivered architecture, SASE becomes the foundation for modern digital transformation.

**Components of SASE**

1. Network as a Service (NaaS): SASE integrates network services such as SD-WAN (Software-Defined Wide Area Network) and cloud connectivity to provide organizations with a flexible and scalable network infrastructure. With NaaS, businesses can optimize network performance, reduce latency, and ensure reliable connectivity across different environments.

2. Security as a Service (SECaaS): SASE incorporates various security services, including secure web gateways, firewall-as-a-service, data loss prevention, and zero-trust network access. By embedding security into the network infrastructure, SASE enables organizations to enforce consistent security policies, protect against threats, and simplify the management of security measures.

3. Zero-Trust Architecture: SASE adopts a zero-trust approach, which assumes that no user or device should be trusted by default, even within the network perimeter. By implementing continuous authentication, access controls, and micro-segmentation, SASE ensures that every user and device is verified before accessing network resources, reducing the risk of unauthorized access and data breaches.

4. Cloud-Native Architecture: SASE leverages cloud-native technologies to provide a scalable, agile, and elastic network and security infrastructure. By transitioning from legacy hardware appliances to software-defined solutions, SASE enables organizations to respond more to changing business requirements, reduce costs, and improve overall efficiency.

Note: Point Solutions

SASE is becoming increasingly popular among organizations because it provides a more flexible and cost-effective approach to networking and security. The traditional approach involves deploying multiple devices or appliances, each with its functions. This approach can be complex, time-consuming, and expensive to manage. On the other hand, SASE simplifies this process by integrating all the necessary functions into a single platform.

Example Technology: Web Security Scanner

security web scanner

Example Product: Cisco Meraki

### Simplified Network Management

One of the standout features of the Cisco Meraki platform is its simplified network management. Gone are the days of complex configurations and time-consuming setups. With Meraki’s cloud-based interface, administrators can manage their entire network from a single dashboard. This ease of use not only saves time but also reduces the need for specialized IT staff, making it an ideal solution for businesses of all sizes.

### Robust Security Features

Security is a top priority for any network, and Cisco Meraki does not disappoint. The platform comes equipped with robust security features, including advanced threat protection, intrusion detection, and automated firmware updates. These features work seamlessly to protect your network from potential threats, ensuring that your data remains secure and your operations run smoothly.

### Scalability and Flexibility

As your business grows, so too does the need for a scalable network solution. Cisco Meraki’s platform is designed with scalability in mind, allowing you to easily add new devices and extend your network without any hassle. Whether you’re expanding to a new office location or integrating additional IoT devices, Meraki’s flexible architecture ensures that your network can grow alongside your business.

### Enhanced Visibility and Analytics

Understanding your network’s performance is crucial for making informed decisions. Cisco Meraki offers enhanced visibility and analytics, providing detailed insights into network usage, device performance, and potential issues. With these analytics, administrators can proactively address problems before they impact operations, optimize resource allocation, and ensure that their network is running at peak efficiency.

### Streamlined Troubleshooting

Troubleshooting network issues can be a daunting task, but Cisco Meraki makes it easier than ever. The platform’s intuitive dashboard provides real-time alerts and diagnostic tools, allowing administrators to quickly identify and resolve issues. This streamlined troubleshooting process minimizes downtime and keeps your network running smoothly.

**SASE Meaning: SASE wraps up**

SASE is a network and security architecture consolidating numerous network and security functions, traditionally delivered as siloed point solutions, into an integrated cloud service. It combines several network and security capabilities along with cloud-native security functions. The functions are produced from the cloud and provided by the SASE vendor.

They are essentially providing a consolidated, platform-based approach to security. We have a cloud-delivered solution consolidating multiple edge network security controls and network services into a unified solution with centralized management and distributed enforcement.

**The appliance-based perimeter**

Even Though there has been a shift to the cloud, the traditional perimeter network security solution has remained appliance-based. The change for moving security controls to the cloud is for better protection and performance, plus ease of deployment and maintenance.

The initial performance of the earlier cloud-delivered solutions has been overcome with the introduction of optimized routing and global footprint. However, there is a split in opinion about performance and protection. Many consider protection and performance prime reasons to remain on-premises and keep the network security solutions on-premises.

 

Secure Access Service Edge • Widget 2

SASE Performance & PoP Path Explorer

Explore how SASE chooses an access path through distributed points of presence and why latency, packet loss and jitter can matter more than raw bandwidth.
USER ACCESS SASE PoPs GLOBAL BACKBONE CLOUD APPLICATION SOURCE User Dublin / Remote POP A Nearby PoP Low-latency option POP B Distant PoP Higher latency 18 ms 46 ms EDGE 1 Backbone EDGE 2 Backbone EDGE 3 Backbone DESTINATION SaaS Application Cloud workload Example: Microsoft 365 PATH QUALITY LATENCY 18 ms PACKET LOSS 0.1% JITTER 3 ms OPTIMAL PATH PERFORMANCE PRINCIPLE Application performance depends on the quality of the path, not simply the amount of available bandwidth.
PATH ANALYSIS

Choose the Best Path

SASE can use distributed points of presence and optimized connectivity to steer traffic toward a suitable application path. Select a PoP or path to explore the decision.

Key Idea The closest PoP is not automatically the best path. Path quality depends on several network characteristics.
Latency
18 ms
Packet Loss
0.1%
Jitter
3 ms
Status
Optimal
Latency The time required for traffic to travel between endpoints. Higher latency can directly affect interactive applications.
Packet Loss Lost packets can cause retransmissions and reduce application efficiency even when plenty of bandwidth remains available.
Jitter Variation in packet arrival time matters particularly for real-time applications such as voice and video.

SASE Definition

SASE Definition with Challenge 1: Managing the Network

Across the entire networking and security industry, everyone sells individual point solutions that are not a holistic joined-up offering. Thinking only about MPLS replacement leads to incremental point solution acquisitions when confronted by digital initiatives, making networks more complex and costly.

Principally, distributed appliances for network and security at every location require additional tasks such as installation, ongoing management, regular updates, and refreshes. This results in far too many security and network configuration points. We see this all the time with NOC and SOC integration efforts.

A: Numerous integration points:

The point-solution approach addresses one issue and requires considerable integration. Therefore, you must constantly add solutions to the stack, likely resulting in management overhead and increased complexity. Let’s say you are searching for a new car. Would you prefer to build the car with all the different parts or buy the already-built one?

In the same way, if we examine the network and security industry, the way it has been geared up presently is provided in parts. It’s your job to support, manage, and build the stack over time and scale it when needed. Fundamentally, it would help if you were an expert in all the different parts. However, if you abstract complexity into one platform, you don’t need to be an expert in everything. SASE is one effective way to abstract management and operational complexity.

B: Required: How SASE solves this

Converging network and security into a single platform does not require multiple integration points. This will eliminate the need to deploy these point solutions and the complexities of managing each. Essentially, with SASE, we can bring each point solution’s functionalities together and place them under one hood—the SASE cloud. SASE merges all of the networking and security capabilities into a single platform.

This way, you now have a holistic joined-up offering. Customers don’t need to perform upgrades or size and scale their network. Instead, all this is done for them in the SASE cloud, creating a fully managed and self-healing architecture. Besides, the convergence is minimal if something goes wrong in one of the SASE Pops. All of this is automatic, and there is no need to set up new tunnels or have administrators step in to perform configurations.

SASE Definition with Challenge 2: Site Connectivity

SD-WAN appliances require other solutions for global connectivity and to connect, secure, and manage mobile users and cloud resources. As a result, many users are turning to Service Providers to handle the integration. The carrier-managed SD-WAN providers integrate a mix of SD-WAN and security devices to form SD-WAN services.

A: Lack of Agility

Unfortunately, this often makes the Service Providers inflexible in accommodating new requests. The telco’s lack of agility and high bandwidth costs will remain problematic. Deploying new locations has been the biggest telco-related frustration, especially when connecting offices outside of the telco’s operating region to the company’s MPLS network. For this, they need to integrate with other telcos.

B: Required: How SASE solves this

SASE handles all of the complexities of management. As a result, the administrative overhead for managing and operating a global network that supports site-to-site connectivity and enhanced security, cloud, and mobility is kept to an absolute minimum.

SASE Definition with Challenge 3: Performance Between Locations

The throughput is primarily determined by latency and packet loss, not bandwidth. Therefore, for an optimal experience for global applications, we must explore ways to manage the latency and packet loss end-to-end for last-mile and middle-mile segments. Most SD-WAN vendors don’t control these segments, affecting application performance and service agility.

Consequently, constant tweaking at the remote ends will be required to attain the best performance for your application. With SD-WAN, we can bundle transports and perform link bonding to solve the last mile. However, this does not create any benefits for the middle mile bandwidth. MPLS will help you overcome the middle-mile problems, but you will likely pay a high price.

A: Required: How SASE solves this

The SASE cloud already has an optimized converged network and security platforms. Therefore, sites need to connect to the nearest SASE PoP. This way, the sites are placed on the global private backbone to take advantage of global route optimization, dynamic path selection, traffic optimization, and end-to-end encryption. The traffic can also be routed over MPLS, directly between sites (not through the SASE PoP), and from IPsec tunnels to third-party devices. The SASE architecture optimizes the last and middle-mile traffic flows.

B: Required: Optimization techniques:

The SASE global backbone uses several techniques to improve network performance, resulting in predictable, consistent latency and packet loss. The SASE cloud has complete control of each PoP and can employ optimizations. It uses proprietary routing algorithms that factor in latency, packet loss, and jitter. These routing algorithms favor performance over cost and select the optimal route for every network packet. This is compared to Internet routing, where metrics don’t consider what is best for the application or the type.

Example TCP Performance Parameters.

SASE Definition with Challenge 4: Cloud Agility

Cloud applications are becoming more critical to organizations, even more so than those hosted in private data centers. When delivering cloud resources, we must consider more than just providing connectivity. In the past, when we spoke about agility, we were concerned only with the addition of new on-premises sites.

However, now, this conversation needs to encompass the cloud. Delivering cloud applications is primarily about providing an application experience that is as responsive as on-premises. However, most SD-WANs have a low response rate for rapidly offering new public cloud infrastructure. MPLS is expensive, rigid, and not built for cloud access.

A: Required: How SASE solves this

SASE natively supports cloud data centers (IaaS) and applications (SaaS) without additional configuration, complexity, or point solutions, enabling built-in cloud connectivity. This further allows the rapid delivery of new public cloud infrastructure.

The SASE PoPs are collocated in the data centers and directly connected to the IXP of the leading IaaS providers, such as Amazon AWS, Microsoft Azure, and Google Cloud Platform. In addition, cloud applications are optimized through SASE’s ability to define the egress points.

This helps exit the cloud application traffic at the points closest to the customer’s application instance. The optimal global routing algorithms can determine the best path from anywhere to the customer’s cloud application instance. This provides optimal performance to the cloud applications regardless of the user’s location.

So, when we talk about performance to the cloud with SASE, the latency to the cloud is comparable to the optimized access provided by the cloud providers, such as AWS Direct Connect or Azure Express Route. So, authentically, SASE provides out-of-the-box cloud performance.

SASE Definition with Challenge 5: Security

The security landscape is constantly evolving. Therefore, network security solutions must develop to form a well-founded landscape. Ransomware and Malware will continue to be the primary security concerns from 2020 onward. Combating the various solutions designed with complex integration points scattered throughout the network domain is challenging for the entire organization.

Security must be part of any WAN transformation initiative. It must protect users and resources regardless of the underlying network, managed through a single-pane-of-glass. However, a bundle of non-integrated security products results in appliance sprawl that hinders your security posture instead of strengthening it. The security solution must defend against emerging threats like malware and ransomware. In addition, it must boost the ability to enforce corporate security policies on mobile users.

Finally, the security solution must also address the increasing cost of buying and managing security appliances and software.

**Security and encryption**

The complexity increases due to the disparate tools required to address the different threat vectors. For example, we have DLP that can be spread across the SWG, CASB, and DLP but with three other teams managing each. What about the impact of encrypted web traffic on the security infrastructure?

The issue is that most internet traffic is now encrypted, and attackers deliver the payloads, deliver command and control instructions, and exfiltrate data over encrypted protocols. Organizations cannot decrypt all network traffic for performance reasons and avoid looking at sensitive employee information. Also, there are issues with the scalability of encrypted traffic management solutions, which can also cause performance issues.

Example Technology: Sensitive Data Protection

Sensitive data protection

Example Technology: Security Backdoors

Backdoor access refers to a hidden method or vulnerability intentionally created within a system or software that allows unauthorized access or control. It is an alternative entry point that bypasses conventional security measures, often undetected.

Using Bash: Bash, short for “Bourne Again SHell,” is a widely used command-line interpreter in Unix-based systems. It provides powerful scripting capabilities, making it a favorite among system administrators and developers. However, this versatility also brings the potential for misuse. This section will explain what a Bash backdoor is and how it functions.

Note: In the following, I created a backdoor on a corporate machine to maintain persistence within the environment. I performed bash script and system configuration using cron jobs. You will then connect to the created backdoor. Here, we demonstrate how to use tools available on standard operating system installations to bypass an organization’s security controls.

Cron jobs, derived from the word “chronos,” meaning time in Greek, are scheduled tasks that run automatically in the background of your server. They follow a specific syntax, using fields to specify when and how often a task should be executed. You can create precise and reliable automated processes by grasping the structure and components of cron jobs.


First, the file called file is deleted with the rm command if it already exists. Next, a special pipe, a new communications channel, is called a file. Any information passed to the bash terminal, such as typed commands, is transmitted to a specific IP address and port using the pipe. The | indicates the point at which the output from one Linux command passes information to the following command. You can create a network connection to a specific machine using this single line, giving a user remote access.

First, errors when running the cron task are ignored and not printed on the screen. Then, the new cronjob is printed to the screen; in this example, the backdoor bash shell will run every minute. The output of the echoed command is then written to the cronfile with crontab. 

SASE Definition with Challenge 6: MPLS and SD-WAN

MPLS does not protect resources and users, certainly not those connected to the Internet. On the other hand, SD-WAN service offerings are not all created equal since many do not include firewall/security features for threat protection to protect all edges—mobile devices, sites, and cloud resources. This lack of integrated security complicates SD-WAN deployments and often leads to Malware getting past the perimeter unnoticed.

Challenge: The cost involved

Security solutions are expensive, and there is never a fixed price. Some security vendors may charge for usage models for which you don’t yet have the quantity. This makes the planning process extraordinarily problematic and complex. As the costs keep increasing, security professionals often trade off point-security solutions due to the associated costs. This is not an effective risk-management strategy.

The security controls are also limited to mobile VPN solutions. More often than not, they are very coarse, forcing IT to open access to all the network resources. Protecting mobile users requires additional security tools like next-generation firewalls (NGFWs), so we have another point solution. In addition, mobile VPN solutions provide no last—or middle-mile optimization.

SASE Meaning: How SASE solves this

SASE converges a complete security stack into the network, allowing it to bring granular control to sites and mobile and cloud resources by enforcing the zero-trust principles for all edges. SASE provides anti-malware protection for both WAN and Internet traffic. In addition, for malware detection and prevention, SASE can offer signature and machine-based learning protection consisting of several integrated anti-malware engines.

For malware communication, SASE can stop the outbound traffic to C&C servers based on reputation feeds and network behavioral analysis. Mobile user traffic is fully protected by SASE’s advanced security services, including NGFW, secure web gateway (SWG), threat prevention, and managed threat detection and response. Furthermore, in the case of mobile, SASE mobile users can dynamically connect to the closest SASE PoP regardless of the location. Again, as discussed previously, the SASE cloud’s relevant optimizations are available for mobile users.

Rethink the WAN: The shift to the cloud, edge computing, and mobility offers new opportunities for IT professionals. Network professionals must rethink their WAN transformation approach to support these digital initiatives. WAN transformation is not just about replacing MPLS with SD-WAN. An all-encompassing solution is needed that provides the proper network performance and security level for enhanced site-to-site connectivity, security, mobile, and cloud.

Example Product: Cisco Umbrella

### What is Cisco Umbrella?

Cisco Umbrella acts as a first line of defense against internet-based threats by leveraging the cloud. It uses DNS (Domain Name System) to block malicious domains, IPs, and URLs before a connection can be established. By analyzing and learning from internet activity patterns, it can predict and prevent potential threats, ensuring that your network remains secure.

### Key Features of Cisco Umbrella

1. **DNS Layer Security**: Cisco Umbrella provides a protective shield at the DNS layer, stopping threats before they reach your network or endpoints. This means that harmful requests are blocked at the source, reducing the risk of malware infections.

2. **Secure Web Gateway**: The solution offers a secure web gateway that inspects web traffic and enforces security policies. It ensures that only safe and compliant traffic is allowed, providing an additional layer of security.

3. **Cloud-Delivered Firewall**: Cisco Umbrella includes a built-in firewall to block unwanted traffic, adding another layer of protection. This firewall can be managed from the cloud, simplifying the process of maintaining network security.

4. **Threat Intelligence**: With real-time threat intelligence updates from Cisco Talos, one of the world’s largest commercial threat intelligence teams, Cisco Umbrella ensures that your defenses are always up to date against the latest threats.

### Benefits of Using Cisco Umbrella

1. **Simplified Security Management**: Being cloud-based, Cisco Umbrella is easy to deploy and manage. There’s no need for complex hardware or software installations, reducing the burden on IT teams.

2. **Improved Visibility**: Cisco Umbrella provides comprehensive insights into internet activity across all devices and locations. This visibility helps in identifying and responding to potential threats swiftly.

3. **Enhanced User Experience**: By blocking malicious content at the DNS layer, users experience faster internet speed and reduced latency, leading to a smoother browsing experience.

4. **Scalability**: Whether you are a small business or a large enterprise, Cisco Umbrella can scale according to your needs. Its cloud-native architecture ensures that it can handle an increasing number of users and devices without compromising on performance.

Summary: SASE Definition

Secure Access Service Edge • Widget 3

SASE Security Decision Explorer

Follow a request from the user through identity and device verification, policy evaluation and security controls before the application access decision is made.
REQUEST User Alice Requests application IDENTITY Verify Identity User / role / MFA DEVICE Device Context Managed / posture POLICY ENGINE Access Decision Evaluate context against policy ALLOW ZTNA application access SWG web security CASB cloud control FWaaS firewall service RESOURCE Application SaaS / Private Resource ZERO TRUST DECISION Identity → Device Context → Policy → Security Control → Application ACCESS ALLOWED
SECURITY DECISION

Access Decision

SASE applies security controls around the user and application rather than assuming that network location automatically means trust.

Key Idea Verify the user, evaluate the device and context, apply policy, then enforce the appropriate access decision.
Current Decision
ALLOW
1. Verify Identity and device context are evaluated before access is granted. Authentication alone does not automatically establish trust.
2. Apply Policy Policy can consider identity, device posture, application, location and other contextual information.
3. Enforce ZTNA, SWG, CASB and FWaaS can provide the appropriate security enforcement for the requested application or traffic.

With the ever-evolving landscape of technology and the increasing demand for secure and efficient networks, a new paradigm has emerged in the realm of network security – SASE, which stands for Secure Access Service Edge. In this blog post, we delved into the definition of SASE, its key components, and its transformative impact on network security.

Understanding SASE

SASE, pronounced “sassy,” is a comprehensive framework that combines network security and wide area networking (WAN) capabilities into a single cloud-based service model. It aims to provide users with secure access to applications and data, regardless of their location or the devices they use. By converging networking and security functions, SASE simplifies the network architecture and enhances overall performance.

The Key Components of SASE

To fully grasp the essence of SASE, it is essential to explore its core components. These include:

1. Secure Web Gateway (SWG): The SWG component of SASE ensures safe web browsing by inspecting and filtering web traffic, protecting users from malicious websites, and enforcing internet usage policies.

2. Cloud Access Security Broker (CASB): CASB provides visibility and control over data as it moves between the organization’s network and multiple cloud platforms. It safeguards against cloud-specific threats and helps enforce data loss prevention policies.

3. Firewall-as-a-Service (FWaaS): FWaaS offers scalable and flexible firewall protection, eliminating the need for traditional hardware-based firewalls. It enforces security policies and controls access to applications and data, regardless of their location.

4. Zero Trust Network Access (ZTNA): ZTNA ensures that users and devices are continuously authenticated and authorized before accessing resources. It replaces traditional VPNs with more granular and context-aware access policies, reducing the risk of unauthorized access.

The Benefits of SASE

SASE brings numerous advantages to organizations seeking enhanced network security and performance:

1. Simplified Architecture: By consolidating various network and security functions, SASE eliminates the need for multiple-point solutions, reducing complexity and management overhead.

2. Enhanced Security: With its comprehensive approach, SASE provides robust protection against emerging threats, ensuring data confidentiality and integrity across the network.

3. Improved User Experience: SASE enables secure access to applications and data from any location, offering a seamless user experience without compromising security.

Conclusion:

In conclusion, SASE represents a paradigm shift in network security, revolutionizing how organizations approach their network architecture. By converging security and networking functions, SASE provides a comprehensive and scalable solution that addresses the evolving challenges of today’s digital landscape. Embracing SASE empowers organizations to navigate the complexities of network security and embrace a future-ready approach.

SD WAN Overlay

SD WAN Overlay

Modern enterprise WANs rarely depend on a single type of connection. Organisations may combine MPLS, broadband Internet, dedicated connectivity, 4G/5G, and cloud access across a large number of sites. The challenge is no longer simply connecting locations; it is building a WAN that can adapt to changing application requirements while maintaining predictable performance, security, and operational control. SD-WAN addresses this challenge by separating the logical network and its policies from the physical transport services underneath it.

An SD-WAN overlay is a virtual networking layer built across one or more physical WAN transports. Traffic can be encapsulated and carried across different underlying networks while the SD-WAN control plane determines how that traffic should be forwarded. This allows organisations to treat different transport services as components of a broader WAN rather than designing the entire architecture around one physical circuit type.

The distinction between the underlay and overlay is fundamental. The underlay provides the physical or IP connectivity between locations, while the overlay provides logical connectivity, routing, policy, and often encryption across those transports. A broadband connection, MPLS circuit, or 5G connection can therefore form part of the underlay while SD-WAN creates a consistent logical network across them.

One of the major benefits is dynamic path selection. Instead of relying exclusively on static routing decisions, SD-WAN can evaluate characteristics such as latency, packet loss, jitter, link availability, and application requirements when selecting a path. Business-critical traffic can therefore be steered towards an appropriate transport while less sensitive traffic may use another available path.

Scalability is another important advantage. New branches, cloud connections, and WAN transports can be incorporated into the SD-WAN architecture without requiring every location to be designed around a completely different physical network. Centralised policy and orchestration can reduce repetitive configuration and provide greater consistency as the environment expands.

The overlay can also provide an important security boundary. SD-WAN deployments commonly use encrypted tunnels between sites or security enforcement points, protecting traffic as it crosses shared or untrusted transport networks. However, encryption should not be confused with complete security: traffic inside an encrypted tunnel may still require firewalling, segmentation, identity controls, threat inspection, and monitoring.

Application awareness is particularly valuable in modern WAN environments. An organisation may have voice and video traffic, SaaS applications, business-critical databases, cloud workloads, and ordinary Internet traffic sharing the same physical transports. SD-WAN policy can classify traffic and apply different forwarding or quality requirements according to the application’s needs.

Operational visibility is equally important. A functioning SD-WAN overlay should make it possible to understand which paths are being used, how transport conditions are changing, whether tunnels are healthy, and how application performance is affected. Monitoring both the underlay and overlay helps distinguish a physical connectivity problem from a policy, tunnel, routing, or application issue.

The result is a WAN architecture in which physical connectivity becomes the foundation rather than the complete design. The underlay supplies transport, while the SD-WAN overlay provides logical connectivity, policy, traffic steering, encryption, and operational control. This separation gives organisations greater flexibility to combine different connectivity options while adapting the WAN to the requirements of distributed applications and users.

SD WAN Overlay

Highlights: SD WAN Overlay

SD-WAN • Interactive Learning

SD-WAN Overlay Explorer

Explore the difference between the physical WAN underlay and the logical SD-WAN overlay.

LOGICAL OVERLAY Branch A SD-WAN Edge Branch B SD-WAN Edge PHYSICAL TRANSPORTS MPLS Private transport Broadband Internet transport LTE / 5G Wireless transport TRANSPORT ABSTRACTION Multiple physical transports can support one logical WAN Physical WAN below • Logical WAN above
Learning Focus

One logical WAN over many transports

SD-WAN separates the logical WAN service from the physical networks that carry it. The underlay provides connectivity while the overlay provides the logical network between SD-WAN edges.

1. Underlay MPLS, broadband and LTE/5G provide the physical connectivity.
2. Overlay SD-WAN creates logical connectivity across those transports.
3. Independence Applications use the logical WAN without needing to understand the underlying transport.
Core idea: The underlay carries traffic. The overlay defines the logical WAN.

Underlay

The physical connectivity layer. Different WAN transports can coexist underneath the SD-WAN fabric.

Overlay

The logical network created between SD-WAN edges across the available physical connectivity.

Transport Independence

The logical WAN does not have to depend on a single physical transport technology.

SD-WAN principle: separate the logical WAN from the physical WAN.

Understanding Overlay Networking

Overlay networking is a revolutionary approach to network design that enables the creation of virtual networks on top of existing physical networks. By abstracting the underlying infrastructure, overlay networks provide a flexible and scalable solution to meet the dynamic demands of modern applications and services. Whether in cloud environments, data centers, or even across geographically dispersed locations, overlay networking opens up a world of possibilities.

Overlay networks act as a virtual layer on the physical network infrastructure. They enable the creation of logical connections independent of the physical network topology. In the context of SD-WAN, overlay networks facilitate the seamless integration of multiple network connections, including MPLS, broadband, and LTE, into a unified and efficient network.

The advantages of overlay networking are manifold:

– First, it allows for seamless network segmentation, enabling different applications or user groups to operate in isolation while sharing the same physical infrastructure. This enhances security and simplifies network management.

– Secondly, overlay networks facilitate deploying advanced network services such as load balancing, firewalling, and encryption without complex changes to the underlying network infrastructure. This abstraction level empowers organizations to adapt and respond rapidly to evolving business needs.

SD‑WAN Overlay — A Virtual WAN Built on Top of Any Transport

An SD‑WAN overlay is the virtual WAN that SD‑WAN creates on top of whatever physical links an enterprise already has. Instead of relying on a single underlay like MPLS, SD‑WAN builds encrypted tunnels across broadband, fiber, LTE/5G, satellite, or MPLS and treats them as one unified logical network. This overlay is where SD‑WAN applies application‑aware routing, dynamic path selection, QoS, and security policies. It is the “software‑defined” part of SD‑WAN — a virtual fabric that abstracts away the complexity of the physical WAN.

Overlay vs Underlay — Clear Separation of Roles

The underlay is the physical transport: broadband, MPLS, LTE/5G, fiber, satellite.

The overlay is the virtual network SD‑WAN builds on top of these transports.

The overlay provides:

  • encrypted tunnels (IPsec or DTLS) between SD‑WAN edges
  • centralized policy enforcement
  • application‑aware routing
  • dynamic failover and load balancing
  • unified QoS across all links

This separation allows SD‑WAN to steer traffic based on real‑time link health (latency, jitter, packet loss) rather than static routing.

Encrypted Tunnels — The Foundation of the Overlay

SD‑WAN overlays are built using encrypted tunnels that run across any IP‑based transport. These tunnels:

  • secure traffic regardless of the underlying link
  • allow consistent routing and segmentation
  • enable full‑mesh or hub‑and‑spoke topologies
  • carry application‑aware metadata for steering decisions

The overlay hides the complexity of the underlay, making broadband or LTE behave like enterprise‑grade WAN circuits.

Dynamic Path Selection — Real‑Time Steering Inside the Overlay

Inside the overlay, SD‑WAN edges continuously measure link performance and steer traffic accordingly. This includes:

  • selecting the best path per application
  • automatic failover when a link degrades
  • load distribution across multiple transports
  • tunnel bonding or packet duplication for critical apps

This dynamic steering is what makes the overlay “intelligent,” ensuring optimal performance for SaaS, VoIP, ERP, and cloud workloads.

Overlay Topologies — Hub‑and‑Spoke, Full‑Mesh, Hybrid

SD‑WAN overlays support multiple topologies depending on business needs:

  • Hub‑and‑spoke — simple, centralized breakout
  • Full‑mesh — direct site‑to‑site tunnels for low‑latency east‑west traffic
  • Hybrid — MPLS for critical apps + broadband/LTE for best‑effort or cloud access

These topologies can be changed centrally without touching individual routers, thanks to the overlay’s software‑defined control plane.

Security Inside the Overlay

Many SD‑WAN platforms embed security directly into the overlay, including:

  • segmentation
  • NGFW functions
  • secure internet breakout
  • integration with CASB, SWG, ZTNA, and SSE stacks

This ensures that the overlay is not just a transport mechanism but a secure, policy‑driven fabric.

Why the SD‑WAN Overlay Matters

The overlay is the reason SD‑WAN delivers:

  • better SaaS and cloud performance
  • reduced MPLS dependency
  • simplified operations
  • improved uptime
  • centralized policy control
  • consistent security across all sites

It transforms WAN management from a hardware problem into a software‑defined architecture.

**So, what exactly is an SD-WAN overlay?**

In simple terms, it is a virtual layer added to the existing network infrastructure. These network overlays connect different locations, such as branch offices, data centers, and the cloud, by creating a secure and reliable network.

1. Tunnel-Based Overlays:

One of the most common types of SD-WAN overlays is tunnel-based overlays. This approach encapsulates network traffic within a virtual tunnel, allowing it to traverse multiple networks securely. Tunnel-based overlays are typically implemented using IPsec or GRE (Generic Routing Encapsulation) protocols. They offer enhanced security through encryption and provide a reliable connection between the SD-WAN edge devices.

Example Technology: IPSec and GRE

Organizations can achieve enhanced network security and improved connectivity by combining GRE and IPSec. The integration allows for creating secure tunnels between networks, ensuring that data transmitted between them remains protected from potential threats. This combination also enables the establishment of Virtual Private Networks (VPNs), enabling secure remote access and seamless connectivity for geographically dispersed teams.

By encrypting GRE tunnels, IPsec provides secure VPN tunnels. This approach offers many benefits, including support for dynamic IGP routing protocols, non-IP protocols, and IP multicast. Furthermore, the headend IPsec termination points can support QoS policies and deterministic routing metrics.

There is built-in redundancy due to the pre-established primary and backup GRE over IPsec tunnels. Static IP addresses are required for the headend site, but dynamic IP addresses are permitted for the remote sites. To differentiate primary tunnels from backup tunnels, routing metrics can be modified slightly to favor one or the other.

GRE with IPsec ipsec plus GRE

2. Segment-Based Overlays:

Segment-based overlays are designed to segment the network traffic based on specific criteria such as application type, user group, or location. This allows organizations to prioritize critical applications and allocate network resources accordingly. By segmenting the traffic, SD-WAN can optimize the performance of each application and ensure a consistent user experience. Segment-based overlays are particularly beneficial for businesses with diverse network requirements.

3. Policy-Based Overlays:

Policy-based overlays enable organizations to define rules and policies that govern the behavior of the SD-WAN network. These overlays use intelligent routing algorithms to dynamically select the most optimal path for network traffic based on predefined policies. By leveraging policy-based overlays, businesses can ensure efficient utilization of network resources, minimize latency, and improve overall network performance.

4. Hybrid Overlays:

Hybrid overlays combine the benefits of both public and private networks. This overlay allows organizations to utilize multiple network connections, including MPLS, broadband, and LTE, to create a robust and resilient network infrastructure. Hybrid overlays intelligently route traffic through the most suitable connection based on application requirements, network availability, and cost. Businesses can achieve high availability, cost-effectiveness, and improved application performance by leveraging mixed overlays.

5. Cloud-Enabled Overlays:

As more businesses adopt cloud-based applications and services, seamless connectivity to cloud environments becomes crucial. Cloud-enabled overlays provide direct and secure connectivity between the SD-WAN network and cloud service providers. These overlays ensure optimized performance for cloud applications by minimizing latency and providing efficient data transfer. Cloud-enabled overlays simplify the management and deployment of SD-WAN in multi-cloud environments, making them an ideal choice for businesses embracing cloud technologies.

**Challenge: The traditional network**

The networks we depend on for business are sensitive to many factors that can result in a slow and unreliable experience. Latency, which refers to the time between a data packet being sent and received, or round-trip time, which is the time it takes for the packet to be sent and for it to get a reply, can be experienced.

We can also experience jitter, the variance in the time delay between data packets in the network, which is a “disruption” in the sending and receiving packets. We have fixed-bandwidth networks that can experience congestion. For example, with five people sharing the same Internet link, each could experience a stable and swift network. Add another 20 or 30 people onto the same link, and the experience will be markedly different.

Google Cloud & SD WAN

SD WAN Cloud Hub

Google Cloud is renowned for its robust infrastructure, scalability, and advanced services. By integrating SD-WAN with Google Cloud, businesses can tap into this powerful ecosystem. They gain access to Google’s global network, enabling optimized routing and lower latency. Additionally, the scalability and flexibility of Google Cloud allow organizations to adapt to changing network demands seamlessly.

Key Advantages:

When SD-WAN Cloud Hub is integrated with Google Cloud, it unlocks a host of advantages. Firstly, it enables organizations to seamlessly connect their branch offices, data centers, and cloud resources, providing a unified network fabric. This integration optimizes traffic flow and enhances application performance, ensuring a consistent user experience.

Key Features and Benefits:

Intelligent Traffic Steering: SD-WAN Cloud Hub with Google Cloud allows organizations to intelligently steer traffic based on application requirements, network conditions, and security policies. This dynamic traffic routing ensures optimal performance and minimizes latency.

Simplified Network Management: The centralized management platform of SD-WAN Cloud Hub simplifies network configuration, monitoring, and troubleshooting. Integration with Google Cloud provides a unified view of the entire network infrastructure, streamlining operations and reducing complexity.

Enhanced Security: SD-WAN Cloud Hub leverages Google Cloud’s security features, such as Cloud Armor and Cloud Identity-Aware Proxy, to protect network traffic and ensure secure communication between branches, data centers, and the cloud.

VPN and Overlay technologies

  • Performance-Based Routing & DMVPN 

Performance-based routing is a dynamic routing technique beyond traditional static routing protocols. It leverages real-time data and network monitoring to make intelligent routing decisions based on latency, bandwidth availability, and network congestion. By constantly analyzing network conditions, performance-based routing algorithms can adapt and reroute traffic to the most efficient paths, ensuring speedy and reliable data transmission.

  • DMVPN Phase 3

DMVPN Phase 3 is the latest iteration of the DMVPN technology, designed to address the limitations of its predecessors. It introduces significant improvements in scalability, routing protocol support, and encryption capabilities. By utilizing multipoint GRE tunnels, DMVPN Phase 3 enables efficient and dynamic communication between multiple sites securely and efficiently.

One of DMVPN Phase 3’s key advantages is its scalability. The introduction of the NHRP (Next Hop Resolution Protocol) redirect allows for the dynamic and efficient allocation of network resources, making it an ideal solution for large-scale deployments. Additionally, DMVPN Phase 3 supports a wide range of routing protocols, including OSPF, EIGRP, and BGP, providing network administrators with flexibility and ease of integration.

Multipoint GRE (mGRE) is an underlying technology that plays a crucial role in DMVPN’s functionality. It enables the establishment of multiple tunnels over a single GRE interface, maximizing network resource utilization. Encapsulating packets in GRE headers, mGRE facilitates traffic routing between remote sites, creating a secure and efficient communication path.

Configuring spokes to terminate multiple headends at one or more hub locations can achieve redundancy. Cryptographic attributes are typically mapped to the tunnel initiated by the remote peer through IPsec tunnel protection..

FlexVPN Site-to-Site Smart Defaults

FlexVPN Site-to-Site Smart Defaults is a feature designed to simplify and streamline the configuration process of site-to-site VPN connections. It provides a set of predefined default values for various parameters, eliminating the need for complex manual configurations. Network administrators can save time and effort by leveraging these smart defaults while ensuring robust security.

One key advantage of FlexVPN Site-to-Site Smart Defaults is its ease of use. Network administrators can quickly deploy secure site-to-site VPN connections without extensive knowledge of complex VPN configurations. The predefined defaults ensure that essential security features are automatically enabled, minimizing the risk of misconfigurations and vulnerabilities.

FlexVPN IKEv2 Routing

FlexVPN IKEv2 routing is a robust routing protocol that combines the flexibility of FlexVPN with the security of IKEv2. It allows for dynamic routing between different sites and simplifies the management of complex network infrastructures. By utilizing the power of cryptographic security and advanced routing techniques, FlexVPN IKEv2 routing ensures secure and efficient communication across networks.

FlexVPN IKEv2 routing offers numerous benefits, making it a preferred choice for network administrators. Firstly, it provides enhanced scalability, allowing networks to grow and adapt to changing requirements quickly. Additionally, the protocol ensures end-to-end security by encapsulating data within secure tunnels, protecting it from unauthorized access. Moreover, FlexVPN IKEv2 routing supports multipoint connectivity, enabling seamless communication between multiple sites.

**Transport Fabric Technology**

SD-WAN leverages transport-independent fabric technology to connect remote locations. This is achieved by using overlay technology. The SDWAN overlay works by tunneling traffic over any transport between destinations within the WAN environment.

This gives authentic flexibility to routing applications across any network portion regardless of the circuit or transport type. This is the definition of transport independence. Having a fabric SDWAN overlay network means that every remote site, regardless of physical or logical separation, is always a single hop away from another. DMPVN works based on transport agnostic design.

SD-WAN vs Traditional WAN

SD-WAN overlays offer several advantages over traditional WANs, including improved scalability, reduced complexity, and better control over traffic flows. They also provide better security, as each site is protected by its dedicated security protocols. Additionally, SD-WAN overlays can improve application performance and reliability and reduce latency.

Key Point: SD-WAN abstracts the underlay

With SD-WAN, the virtual WAN overlays are abstracted from the physical device’s underlay. Therefore, the virtual WAN overlays can take on topologies independent of each other without being pinned to the configuration of the underlay network. SD-WAN changes how you map application requirements to the network, allowing for the creation of independent topologies per application.

For example, mission-critical applications may use expensive leased lines, while lower-priority applications can use inexpensive best-effort Internet links. This can all change on the fly if specific performance metrics are unmet.

Previously, the application had to match and “fit” into the network with the legacy WAN, but with an SD-WAN, the application now controls the network topology. Multiple independent topologies per application are a crucial driver for SD-WAN.

Example Technology: PTP GRE

Point to Point GRE, or Generic Routing Encapsulation, is a protocol that encapsulates and transports various network layer protocols over an IP network. Providing a virtual point-to-point link enables secure and efficient communication between remote networks. Point to Point GRE offers a flexible and scalable solution for organizations seeking to establish secure connections over public or private networks.

SD-WAN combines transports, SDWAN overlay, and underlay

Look at it this way. With an SD-WAN topology, there are different levels of networking. There is an underlay network, the physical infrastructure, and an SDWAN overlay network. The physical infrastructure is the router, switches, and WAN transports; the overlay network is the virtual WAN overlays.

The SDWAN overlay presents a different network to the application. For example, the voice overlay will see only the voice overlay. The logical virtual pipe the overlay creates and the application sees differs from the underlay.

An SDWAN overlay network is a virtual or logical network created on top of an existing physical network. The internet, which connects many nodes via circuit switching, is an example of an SDWAN overlay network. An overlay network is any virtual layer on top of physical network infrastructure.

  • Consider an SDWAN overlay as a flexible tag.

This may be as simple as a virtual local area network (VLAN) but typically refers to more complex virtual layers from an SDN or an SD-WAN). Think of an SDWAN overlay as a tag so that building the overlays is not expensive or time-consuming. In addition, you don’t need to buy physical equipment for each overlay as the overlay is virtualized and in the software.

Similar to software-defined networking (SDN), the critical part is that SD-WAN works by abstraction. All the complexities are abstracted into application overlays. For example, application type A can use this SDWAN overlay, and application type B can use that SDWAN overlay. 

  • I.P. and port number, orchestrations, and end-to-end

Recent application requirements drive a new type of WAN that more accurately supports today’s environment with an additional layer of policy management. The world has moved away from looking at I.P. addresses and Port numbers used to identify applications and made the correct forwarding decision. 

Example Product: Cisco Meraki

**Section 1: Simplified Network Management**

One of the standout features of the Cisco Meraki platform is its user-friendly interface. Gone are the days of complex configurations and cumbersome setups. With Meraki, IT administrators can manage their entire network from a single, intuitive dashboard. This centralized management capability allows for real-time monitoring, troubleshooting, and updates, all from the cloud. The simplicity of the platform means that even those with limited technical expertise can effectively manage and optimize their network.

**Section 2: Robust Security Features**

In today’s digital landscape, security is paramount. Cisco Meraki understands this and has built comprehensive security features into its platform. From advanced malware protection to intrusion prevention and content filtering, Meraki offers a multi-layered approach to cybersecurity. The platform also includes built-in security analytics, providing IT teams with valuable insights to proactively address potential threats. This level of security ensures that your network remains protected against both internal and external vulnerabilities.

**Section 3: Scalability and Flexibility**

Another significant advantage of the Cisco Meraki platform is its scalability. As your business grows, so too can your network. Meraki’s cloud-based nature allows for seamless integration of new devices and locations without the need for extensive hardware upgrades. This flexibility makes it an ideal solution for businesses of all sizes, from small startups to large multinational corporations. The platform’s ability to adapt to changing needs ensures that it can grow alongside your business.

**Section 4: Comprehensive Support and Training**

Cisco Meraki doesn’t just provide a platform; it offers a complete ecosystem of support and training. From comprehensive documentation and online tutorials to live webinars and a dedicated support team, Meraki ensures that you have all the resources you need to make the most of its platform. This commitment to customer success means that you’re never alone in your network management journey.

Challenges to Existing WAN

Traditional WAN architectures consist of private MPLS links complemented with Internet links as a backup. Standard templates in most Service Provider environments are usually broken down into Bronze, Silver, and Gold SLAs. 

However, these types of SLA do not fit all geographies and often should be fine-tuned per location. Capacity, reliability, analytics, and security are all central parts of the WAN that should be available on demand. Traditional infrastructure is very static, and bandwidth upgrades and service changes require considerable time processing and locking agility for new sites.

It’s not agile enough, and nothing can be performed on the fly to meet the growing business needs. In addition, the cost per bit for the private connection is high, which is problematic for bandwidth-intensive applications, especially when the upgrades are too costly and can’t be afforded. 

  • A distributed world of dissolving perimeters

Perimeters are dissolving, and the world is becoming distributed. Applications require a WAN to support distributed environments along with flexible network points. Centralized-only designs result in suboptimal traffic engineering and increased latency. Increased latency disrupts the application performance, and only a particular type of content can be put into a Content Delivery Network (CDN). CDN cannot be used for everything.

Traditional WANs are operationally complex; people likely perform different network and security functions. For example, you may have a DMVPN, Security, and Networking specialist. Some wear all hats, but they are few and far between. Different hats have different ideas, and agreeing on a minor network change could take ages.

  • The World of SD-WAN Static Network-Based

SD-WAN replaces traditional WAN routers that are agnostic to the underlying transport technology. You can use various link types, such as MPLS, LTE, and broadband. All combined. Based on policies generated by the business, SD-WAN enables load sharing across different WAN connections that more efficiently support today’s application environment.

It pulls policy and intelligence out of the network and places them into an end-to-end solution orchestrated by a single pane of glass. SDN-WAN is not just about tunnels. It consists of components that work together to simplify network operations while meeting all bandwidth and resilience requirements.

Centralized network points are no longer adequate; we need network points positioned where they make the most sense for the application and user. Backhauling traffic to a central data center is illogical, and connecting remote sites to a SaaS or IaaS model over the public Internet is far more efficient. The majority of enterprises prefer to connect remote sites directly to cloud services. So why not let them do this in the best possible way?

A new style of WAN and SD-WAN

We require a new WAN style and a shift from a network-based approach to an application-based approach. The new WAN no longer looks solely at the network to forward packets. Instead, it looks at the business application and decides how to optimize it with the correct forwarding behavior. This new style of forwarding is problematic with traditional WAN architecture.

Making business logic decisions with IP and port number information is challenging. Standard routing is packet by packet and can only set part of the picture. They have routing tables and perform forwarding but essentially operate on their little island, losing out on a holistic view required for accurate end-to-end decision-making. An additional layer of information is needed.

A controller-based approach offers the necessary holistic view. We can now make decisions based on global information, not solely on a path-by-path basis. Getting all the routing information and compiling it into a dashboard to make a decision is much more efficient than making local decisions that only see parts of the network. 

From a customer’s viewpoint, what would the perfect WAN look like if you could roll back the clock and start again?   

SD WAN Overlay

SD-WAN • Interactive Learning

SD-WAN Path Steering Explorer

See how SD-WAN can compare available paths using latency, packet loss, jitter and business policy.

APPLICATION PATH Branch A SD-WAN Edge SaaS / Cloud Application MPLS 28 ms • 0.1% loss Broadband 18 ms • 0.4% loss LTE / 5G 42 ms • 1.2% loss PATH QUALITY Latency • Loss • Jitter • Policy SELECTED: MPLS
Learning Focus

SD-WAN compares available paths

SD-WAN can continuously evaluate the quality of available paths. The decision is not simply based on which transport exists.

28 Latency ms
0.1% Packet Loss
4 Jitter ms
Path quality Latency, packet loss and jitter provide measurements of current path conditions.
Application requirements Different applications can require different network characteristics.
Policy Business policy can influence which available path should be preferred.
Current decision: MPLS is selected because it currently provides the preferred combination of quality and policy.

Measure

SD-WAN monitors characteristics such as latency, packet loss and jitter across available paths.

Compare

The available paths can be compared against application requirements and policy.

Steer

Traffic can be directed toward a path that better satisfies the current requirements.

SD-WAN principle: path selection can be based on real-time network conditions and application policy — not simply the transport type.

Introducing the SD-WAN Overlay

SD-WAN decouples (separates) the WAN infrastructure, whether physical or virtual, from its control plane mechanism and allows applications or application groups to be placed into virtual WAN overlays. The separation will enable us to bring many enhancements and improvements to a WAN that has had little innovation in the past compared to the rest of the infrastructure, such as server and storage modules.

With server virtualization, several virtual machines create application isolation on a physical server. Applications placed in VMs operate in isolation, yet the VMs are installed on the same physical hosts.

Consider SD-WAN to operate with similar principles. Each application or group can operate independently when traversing the WAN to endpoints in the cloud or other remote sites. These applications are placed into a virtual SDWAN overlay.

Overlay Networking

Overlay networking is an approach to computer networking that involves building a layer of virtual networks on top of an existing physical network. This approach improves the underlying infrastructure’s scalability, performance, and security. It also allows for the creation of virtual networks that span multiple physical networks, allowing for greater flexibility in traffic routes.

**Virtualization**

At the core of overlay networking is the concept of virtualization. This involves separating the physical infrastructure from the virtual networks, allowing greater control over allocating resources. This separation also allows the creation of virtual network segments that span multiple physical networks. This provides an efficient way to route traffic and the ability to provide additional security and privacy measures.

**Underlay network**

A network underlay is a physical infrastructure that provides the foundation for a network overlay, a logical abstraction of the underlying physical network. The network underlay provides the physical transport of data between nodes, while the overlay provides logical connectivity.

The network underlay can comprise various technologies, such as Ethernet, Wi-Fi, cellular, satellite, and fiber optics. It is the foundation of a network overlay and essential for its proper functioning. It provides data transport and physical connections between nodes. It also provides the physical elements that make up the infrastructure, such as routers, switches, and firewalls.

Example: DMVPN over IPSec

Understanding DMVPN

DMVPN is a dynamic VPN technology that simplifies establishing secure connections between multiple sites. Unlike traditional VPNs, which require point-to-point tunnels, DMVPN uses a hub-and-spoke architecture, allowing any-to-any connectivity. This flexibility enables organizations to quickly scale their networks and accommodate dynamic changes in their infrastructure.

On the other hand, IPsec provides a robust framework for securing IP communications. It offers encryption, authentication, and integrity mechanisms, ensuring that data transmitted over the network remains confidential and protected against unauthorized access. IPsec is a widely adopted standard that is compatible with various network devices and software, making it an ideal choice for securing DMVPN connections.

The combination of DMVPN and IPsec brings numerous benefits to organizations. Firstly, DMVPN’s dynamic nature allows for easy scalability and improved network resiliency. New sites can be added seamlessly without the need for manual configuration changes. Additionally, DMVPN over IPsec provides strong encryption, ensuring the confidentiality of sensitive data. Moreover, DMVPN’s any-to-any connectivity optimizes network traffic flow, enhancing performance and reducing latency.

 

Overlay networking
Diagram: Overlay networking. Source Researchgate.

Key Challenges: Driving Overlay Networking & SD-WAN

**Challenge: We need more bandwidth**

Modern businesses demand more bandwidth than ever to connect their data, applications, and services. As a result, we have many things to consider with the WAN, such as regulations, security, visibility, branch, data center sites, remote workers, internet access, cloud, and traffic prioritization. They were driving the need for SD-WAN.

The concepts and design principles of creating a wide area network (WAN) to provide resilient and optimal transit between endpoints have continuously evolved. However, the driver behind building a better WAN is to support applications that demand performance and resiliency.

**Challenge: Suboptimal traffic flow**

The optimal route will be the fastest or most efficient and, therefore, preferred to transfer data. Sub-optimal routes will be slower and, hence, not the selected route. Centralized-only designs resulted in suboptimal traffic flow and increased latency, which will degrade application performance.

A key point to note is that traditional networks focus on centralized points in the network that all applications, network, and security services must adhere to. These network points are fixed and cannot be changed.

**Challenge: Network point intelligence**

However, the network should evolve to have network points positioned where it makes the most sense for the application and user, not based on a previously validated design for a different application era. For example, many branch sites do not have local Internet breakouts.

So, for this reason, we backhauled internet-bound traffic to secure, centralized internet portals at the H.Q. site. As a result, we sacrificed the performance of Internet and cloud applications. Designs that place the H.Q. site at the center of connectivity requirements inhibit the dynamic access requirements for digital business.

**Challenge: Hub and spoke drawbacks**

Simple spoke-type networks are sub-optimal because you always have to go to the center point of the hub and then out to the machine you need rather than being able to go directly to whichever node you need. As a result, the hub becomes a bottleneck in the network as all data must go through it. With a more scattered network using multiple hubs and switches, a less congested and more optimal route could be found between machines.

Cisco SD WAN Overlay
Diagram: Cisco SD-WAN overlay. Source Network Academy

The Fabric:

The word fabric comes from the fact that there are many paths to move from one server to another to ease balance and traffic distribution. SDN aims to centralize the order that enables the distribution of the flows over all the fabric paths. Then, we have an SDN controller device. The SDN controller can also control several fabrics simultaneously, managing intra and inter-datacenter flows.

SD-WAN is used to control and manage a company’s multiple WANs. There are different types of WAN: Internet, MPLS, LTE, DSL, fiber, wired network, circuit link, etc. SD-WAN uses SDN technology to control the entire environment. Like SDN, the data plane and control plane are separated. A centralized controller must be added to manage flows, routing or switch policies, packet priority, network policies, etc. SD-WAN technology is based on overlay, meaning nodes representing underlying networks.

Centralized logic:

In a traditional network, each device’s transport functions and controller layer are resident. This is why any configuration or change must be done box-by-box. Configuration was carried out manually or, at the most, an Ansible script. SD-WAN brings Software-Defined Networking (SDN) concepts to the enterprise branch WAN.

Software-defined networking (SDN) is an architecture, whereas SD-WAN is a technology that can be purchased and built on SDN’s foundational concepts. SD-WAN’s centralized logic stems from SDN. SDN separates the control from the data plane and uses a central controller to make intelligent decisions, similar to the design that most SD-WAN vendors operate.

A holistic view:

The controller and the SD-WAN overlay have a holistic view. The controller supports central policy management, enabling network-wide policy definitions and traffic visibility. The SD-WAN edge devices perform the data plane. The data plane is where simple forwarding occurs, and the control plane, which is separate from the data plane, sets up all the controls for the data plane to forward.

Like SDN, the SD-WAN overlay abstracts network hardware into a control plane with multiple data planes to make up one large WAN fabric. As the control layer is abstracted and decoupled above the physicals and running in software, services can be virtualized and delivered from a central location to any point on the network.

SD-WAN Overlay Features

SD-WAN Overlay Feature 1: Combining the transports:

At its core, SD-WAN shapes and steers application traffic across multiple WAN means of transport. Building on the concept of link bonding to combine numerous means of transport and transport types, the SD-WAN overlay improves the idea by moving the functionality up the stack—first, SD-WAN aggregates last-mile services, representing them as a single pipe to the application.SD-WAN allows you to combine all transport links into one big pipe. SD-WAN is transport agnostic. As it works by abstraction, it does not care what transport links you have. Maybe you have MPLS, private Internet, or LTE. It can combine all these or use them separately.

SD-WAN Overlay Feature 2: location:

From a central location, SD-WAN pulls all of these WAN resources together, creating one large WAN fabric that allows administrators to slice up the WAN to match the application requirements that sit on top. Different applications traverse the WAN, so we need the WAN to react differently. For example, if you’re running a call center, you want a low delay, latency, and high availability with Voice traffic. You may wish to use this traffic to use an excellent service-level agreement path.

SD WAN traffic steering
Diagram: SD-WAN traffic steering. Source Cisco.

SD-WAN Overlay Feature 3: steering:

Traffic steering may also be required: voice traffic to another path if, for example, the first Path is experiencing high latency. If it’s not possible to steer traffic automatically to a link that is better performing, run a series of path remediation techniques to try to improve performance. File transfer differs from real-time Voice: you can tolerate more delay but need more B/W. Here, you may want to use a combination of WAN transports ( such as customer broadband and LTE ) to achieve higher aggregate B/W.

This also allows you to automatically steer traffic over different WAN transports when there is a deflagration on one link. With the SD-WAN overlay, we must start thinking about paths, not links.

SD-WAN Overlay Feature 4: decisions:

At its core, SD-WAN enables real-time application traffic steering over any link, such as broadband, LTE, and MPLS, assigning pre-defined policies based on business intent. Steering policies support many application types, making intelligent decisions about how WAN links are utilized and which paths are taken.

The concept of an underlay and overlay are not new, and SD-WAN borrows these designs. First, the underlay is the physical or virtual world, such as the physical infrastructure. Then, we have the overlay, where all the intelligence can be set. The SDWAN overlay represents the virtual WANs that hold your different applications.

A virtual WAN overlay enables us to steer traffic and combine all bandwidths. Similar to how applications are mapped to V.M. in the server world, with SD-WAN, each application is mapped to its own virtual SD-WAN overlay. Each virtual SD-WAN overlay can have its own SD-WAN security policies, topologies, and performance requirements.

SD-WAN Overlay Feature 5:-Aware Routing Capabilities

Not only do we need application visibility to forward efficiently over either transport, but we also need the ability to examine deep inside the application and look at the sub-applications. For example, we can determine whether Facebook chat is over regular Facebook. This removes the application’s mystery and allows you to balance loads based on sub-applications. It’s like using a scalpel to configure the network instead of a sledgehammer.

SD-WAN Overlay Feature 6: Of Integration With Existing Infrastructure

The risk of introducing new technologies may come with a disruptive implementation strategy. Loss of service damages more than the engineer’s reputation. It hits all areas of the business. The ability to seamlessly insert new sites into existing designs is a vital criterion. With any network change, a critical evaluation is to know how to balance risk with innovation while still meeting objectives.

How aligned is marketing content to what’s happening in reality? It’s easy for marketing materials to implement their solution at Layer 2 or 3! It’s an entirely different ball game doing this. SD-WAN carries a certain amount of due diligence. One way to read between the noise is to examine who has real-life deployments with proven Proof of concept (POC) and validated designs. Proven POC will help you guide your transition in a step-by-step manner.

SD-WAN Overlay Feature 7: Specific Routing Topologies

Every company has different requirements for hub and spoke full mesh and Internet PoP topologies. For example, Voice should follow a full mesh design, while Data requires a hub and spokes connecting to a central data center. Nearest service availability is the key to improved performance, as there is little we can do about the latency Gods except by moving services closer together. 

SD-WAN Overlay Feature 8: Device Management & Policy Administration

The manual box-by-box approach to policy enforcement is not the way forward. It’s similar to stepping back into the Stone Age to request a catered flight. The ability to tie everything to a template and automate enables rapid branch deployments, security updates, and configuration changes. The optimal solutions have everything in one place and can dynamically push out upgrades.

SD-WAN Overlay Feature 9: Available With Automatic Failovers

You cannot apply a singular viewpoint to high availability. An end-to-end solution should address the high availability requirements of the device, link, and site level. WANs can fail quickly, but this requires additional telemetry information to detect failures and brownout events. 

SD-WAN Overlay Feature 10: On All Transports

Irrespective of link type, whether MPLS, LTE, or the Internet, we need the capacity to encrypt on all those paths without the excess baggage of IPsec. Encryption should happen automatically, and the complexity of IPsec should be abstracted.

**Application-Orientated WAN**

Push to the cloud:  

When geographically dispersed users connect back to central locations, their consumption triggers additional latency, degrading the application’s performance. No one can get away from latency unless we find ways to change the speed of light. One way is to shorten the link by moving to cloud-based applications.

The push to the cloud is inevitable. Most businesses are now moving away from on-premise in-house hosting to cloud-based management. Nevertheless, the benefits of moving to the cloud are manifold. It is easier for so many reasons.

The ready-made global footprint enables the usage of SaaS-based platforms that negate the drawbacks of dispersed users tromboning to a central data center. This software is pivotal to millions of businesses worldwide, which explains why companies such as Capsifi are so popular.

Logically positioned cloud platforms are closer to the mobile user. It’s increasingly far more efficient from the technical and business standpoint to host these applications in the cloud, which makes them available over the public Internet.

Bandwidth intensive applications:

Richer applications, multimedia traffic, and growth in the cloud application consumption model drive the need for additional bandwidth. Unfortunately, we can only fit so much into a single link. The congestion leads to packet drops, ultimately degrading application performance and user experience. In addition, most applications ride on TCP, yet TCP was not designed with performance.

Organic growth:

Organic business growth is a significant driver of additional bandwidth requirements. The challenge is that existing network infrastructures are static and unable to respond to this growth in a reasonable period adequately. The last mile of MPLS locks you in and kills agility. Circuit lead times impede the organization’s productivity and create an overall lag.

Costs:

A WAN virtualization solution should be simple. To serve the new era of applications, we need to increase the link capacity by buying more bandwidth. However, nothing is as easy as it may seem. The WAN is one of the network’s most expensive parts, and employing link oversubscription to reduce congestion is too costly.

Furthermore, bandwidth comes at a cost, and the existing TDM-based MPLS architectures cannot accommodate application demands. 

Traditional MPLS is feature-rich and offers many benefits. No one doubts this fact. MPLS will never die. However, it comes at a high cost for relatively low bandwidth. Unfortunately, MPLS’s price and capabilities are not a perfect couple.

Hybrid connectivity:

Since there is not one stamp for the entire world, similar applications will have different forwarding preferences. Therefore, application flows are dynamic and change depending on user consumption. Furthermore, the MPLS, LTE, and Internet links often complement each other since they support different application types.

For example, Storage and Big data replication traffic are forwarded through the MPLS links, while cheaper Internet connectivity is used for standard Web-based applications.

Limitations of protocols:

When left to its defaults, IPsec is challenged by hybrid connectivity. IPSec architecture is point-to-point, not site-to-site. As a result, it doesn’t natively support redundant uplinks. Complex configurations are required when sites have multiple uplinks to multiple providers.

By default, IPsec is not abstracted; one session cannot be used over multiple uplinks, causing additional issues with transport failover and path selection. It’s a Swiss Army knife of features, and much of IPSec’s complexities should be abstracted. Secure tunnels should be torn up and down immediately, and new sites should be incorporated into a secure overlay without much delay or manual intervention. 

Internet of Things (IoT):

Security and bandwidth consumption are key issues when introducing IP-enabled objects and IoT access technologies. IoT is all about Data and will bring a shed load of additional overheads for networks to consume. As millions of IoT devices come online, how efficiently do we segment traffic without complicating the network design further? Complex networks are hard to troubleshoot, and simplicity is the mother of all architectural success. Furthermore, much IoT processing requires communication to remote IoT platforms. How do we account for the increased signaling traffic over the WAN? The introduction of billions of IoT devices leaves many unanswered questions.

Branch NFV:

There has been strong interest in infrastructure consolidation by deploying Network Function Virtualization (NFV) at the branch. Enabling on-demand service and chaining application flows are key drivers for NFV. However, traditional service chaining is static since it is bound to a particular network topology. Moreover, it is typically built through manual configuration and is prone to human error.

 SD-WAN overlay path monitoring:

SD-WAN monitors the paths and the application performance on each link (Internet, MPLS, LTE ) and then chooses the best path based on real-time conditions and the business policy. In summary, the underlay network is the physical or virtual infrastructure above which the overlay network is built. An SDWAN overlay network is a virtual network built on top of an underlying Network infrastructure/Network layer (the underlay).

Controller-based policy:

An additional layer of information is needed to make more intelligent decisions about how and where to forward application traffic. SD-WAN offers a controller-based policy approach that incorporates a holistic view.

A central controller can now make decisions based on global information, not solely on a path-by-path basis with traditional routing protocols.  Getting all the routing information and compiling it into the controller to make a decision is much more efficient than making local decisions that only see a limited part of the network.

The SD-WAN Controller provides physical or virtual device management for all SD-WAN Edges associated with the controller. This includes, but is not limited to, configuration and activation, IP address management, and pushing down policies onto SD-WAN Edges located at the branch sites.

SD-WAN Overlay Case Study:

Personal Note: I recently consulted for a private enterprise. Like many enterprises, they have many applications, both legacy and new. No one knew about courses and applications running over the WAN; visibility was low. For the network design, the H.Q. has MPLS and Direct Internet access. Nothing is new here; this design has been in place for the last decade. All traffic is backhauled to the HQ/MPLS headend for security screening. The security stack, including firewalls, IDS/IPS, and anti-malware, was in the H.Q. The remote sites have high latency and limited connectivity options.

More importantly, they are transitioning their ERP system to the cloud. As apps move to the cloud, they want to avoid fixed WAN, a big driver for a flexible SD-WAN solution. They also have remote branches, which are hindered by high latency and poorly managed IT infrastructure. But they don’t want an I.T. representative at each site location. They have heard that SD-WAN has a centralized logic and can view the entire network from one central location. These remote sites must receive large files from the H.Q.; the branch sites’ transport links are only single-customer broadband links.

Some remote sites have LTE, and the bills are getting more significant. The company wants to reduce costs with dedicated Internet access or customer/business broadband. They have heard that you can combine different transports with SD-WAN and have several path remediations on degraded transports for better performance. So, they decided to roll out SD-WAN. From this new architecture, they gained several benefits.

SD-WAN Visibility

When your business-critical applications operate over different provider networks, troubleshooting and finding the root cause of problems becomes more challenging. So, visibility is critical to business. SD-WAN allows you to see network performance data in real-time and is essential for determining where packet loss, latency, and jitter are occurring so you can resolve the problem quickly.

You also need to be able to see who or what is consuming bandwidth so you can spot intermittent problems. For all these reasons, SD-WAN visibility needs to go beyond network performance metrics and provide greater insight into the delivery chains that run from applications to users.

  • Understand your baselines:

Visibility is needed to complete the network baseline before the SD-WAN is deployed. This enables the organization to understand existing capabilities, the norm, what applications are running, the number of sites connected, what service providers used, and whether they’re meeting their SLAs. Visibility is critical to obtaining a complete picture, so teams understand how to optimize the business infrastructure. SD-WAN gives you an intelligent edge, so you can see all the traffic and act on it immediately.

First, look at the visibility of the various flows, the links used, and any issues on those links. Then, if necessary, you can tweak the bonding policy to optimize the traffic flow. Before the rollout of SD-WAN, there was no visibility into the types of traffic, and different apps used what B.W. They had limited knowledge of WAN performance.

  • SD-WAN offers higher visibility:

With SD-WAN, they have the visibility to control and class traffic on layer seven values, such as what URL you are using and what Domain you are trying to hit, along with the standard port and protocol. All applications are not equal; some run better on different links. If an application is not performing correctly, you can route it to a different circuit. With the SD-WAN orchestrator, you have complete visibility across all locations, all links, and into the other traffic across all circuits. 

  • SD-WAN High Availability:

Any high-availability solution aims to ensure that all network services are resilient to failure. It aims to provide continuous access to network resources by addressing the potential causes of downtime through functionality, design, and best practices. The previous high-availability design was active and passive with manual failover. It was hard to maintain, and there was a lot of unused bandwidth. Now, they use resources more efficiently and are no longer tied to the bandwidth of the first circuit.

There is a better granular application failover mechanism. You can also select which apps are prioritized if a link fails or when a certain congestion ratio is hit. For example, you have LTE as a backup, which can be expensive. So applications marked high priority are steered over the backup link, but guest WIFI traffic isn’t.  

  • Flexible topology:

Before, they had a hub-and-spoke MPLS design for all applications. They wanted a complete mesh architecture for some applications, kept the existing hub, and spoke for others. However, the service provider couldn’t accommodate the level of granularity that they wanted.

With SD-WAN, they can choose topologies that are better suited to the application type. As a result, the network design is now more flexible and matches the application than the application matching a network design it doesn’t want.

Types of SD-WAN

The market for branch office wide-area network functionality is shifting from dedicated routing, security, and WAN optimization appliances to feature-rich SD-WAN. As a result, WAN edge infrastructure now incorporates a widening set of network functions, including secure routers, firewalls, SD-WAN, WAN path control, and WAN optimization, along with traditional routing functionality. Therefore, consider the following approach to deploying SD-WAN.

1. Application-based approach

With SD-WAN, we are shifting from a network-based approach to an application-based approach. The new WAN no longer looks solely at the network to forward packets. Instead, it looks at the business requirements and decides how to optimize the application with the correct forwarding behavior. This new way of forwarding would be problematic when using traditional WAN architectures.

Making business logic decisions with I.P. and port number information is challenging. Standard routing is the most common way to forward application traffic today, but it only assesses part of the picture when making its forwarding decision. 

These devices have routing tables to perform forwarding. Still, with this model, they operate and decide on their little island, losing the holistic view required for accurate end-to-end decision-making.  

2. SD-WAN: Holistic decision

The WAN must start making decisions holistically. It should not be viewed as a single module in the network design. Instead, it must incorporate several elements it has not integrated to capture the correct per-application forwarding behavior. The ideal WAN should be automatable to form a comprehensive end-to-end solution centrally orchestrated from a single pane of glass.

Managed and orchestrated centrally, this new WAN fabric is transport agnostic. It offers application-aware routing, regional-specific routing topologies, encryption on all transports regardless of link type, and high availability with automatic failover. All of these will be discussed shortly and are the essence of SD-WAN.  

3. SD-WAN and central logic        

Besides the virtual SD-WAN overlay, another key SD-WAN concept is centralized logic. Upon examining a standard router, local routing tables are computed from an algorithm to forward a packet to a given destination.

It receives routes from its peers or neighbors but computes paths locally and makes local routing decisions. The critical point to note is that everything is calculated locally. SD-WAN functions on a different paradigm.

Rather than using distributed logic, it utilizes centralized logic. This allows you to view the entire network holistically and with a distributed forwarding plane that makes real-time decisions based on better metrics than before.

This paradigm enables SD-WAN to see how the flows behave along the path. They are taking the fragmented control approach and centralizing it while benefiting from a distributed system. 

The SD-WAN controller, which acts as the brain, can set different applications to run over different paths based on business requirements and performance SLAs, not on a fixed topology. So, for example, if one path does not have acceptable packet loss and latency is high, we can move to another path dynamically.

4. Independent topologies

SD-WAN has different levels of networking and brings the concepts of SDN into the Wide Area Network. Similar to SDN, we have an underlay and an overlay network with SD-WAN. The WAN infrastructure, either physical or virtual, is the underlay, and the SDWAN overlay is in software on top of the underlay where the applications are mapped.

This decoupling or separation of functions allows different application or group overlays. Previously, the application had to work with a fixed and pre-built network infrastructure. With SD-WAN, the application can choose the type of topology it wants, such as a full mesh or hub and spoke. The topologies with SD-WAN are much more flexible.

5. The SD-WAN overlay

SD-WAN optimizes traffic over multiple available connections. It dynamically steers traffic to the best available link. Suppose the available links show any transmission issues. In that case, it will immediately transfer to a better path or apply remediation to a link if, for example, you only have a single link. SD-WAN delivers application flows from a source to a destination based on the configured policy and best available network path. A core concept of SD-WAN is overlaid.

SD-WAN solutions provide the software abstraction to create the SD-WAN overlay and decouple network software services from the underlying physical infrastructure. Multiple virtual overlays may be defined to abstract the underlying physical transport services, each supporting a different quality of service, preferred transport, and high availability characteristics.

6. Application mapping

Application mapping also allows you to steer traffic over different WAN transports. This steering is automatic and can be implemented when specific performance metrics are unmet. For example, if Internet transport has a 15% packet loss, the policy can be set to steer all or some of the application traffic over to better-performing MPLS transport.

Applications are mapped to different overlays based on business intent, not infrastructure details like IP addresses. When you think about overlays, it’s common to have, on average, four overlays. For example, you may have a gold, platinum, and bronze SDWAN overlay, and then you can map the applications to these overlays.

The applications will have different networking requirements, and overlays allow you to slice and dice your network if you have multiple application types. 

SD-WAN & WAN metrics

SD-WAN captures metrics that go far beyond the standard WAN measurements. For example, the traditional method measures packet loss, latency, and jitter metrics to determine path quality. These measurements are insufficient for routing protocols that only make the packet flow decision at layer 3 of the OSI model.

As we know, layer 3 of the OSI model lacks intelligence and misses the overall user experience. We must start looking at application transactions rather than relying on bits, bytes jitter, and latency.

SD-WAN incorporates better metrics beyond those a standard WAN edge router considers. These metrics may include application response time, network transfer, and service response time. Some SD-WAN solutions monitor each flow’s RTT, sliding windows, and ACK delays, not just the I.P. or TCP. This creates a more accurate view of the application’s performance.

Overlay Use Case: DMVPN Dual Cloud

Exploring Single Hub Dual Cloud Configuration

The Single Hub, Dual Cloud configuration, is a DMVPN setup in which a central hub site connects to two or more cloud service providers simultaneously. This configuration offers several advantages, such as increased redundancy, improved performance, and enhanced security.

By connecting to multiple cloud service providers, the Single Hub Dual Cloud configuration ensures redundancy if one provider experiences an outage. This redundancy enhances network availability and minimizes the risk of downtime, providing a robust and reliable network infrastructure.

With the Single Hub Dual Cloud configuration, network traffic can be load-balanced across multiple cloud service providers. This load balancing distributes the workload evenly, optimizing network performance and preventing bottlenecks. It allows for efficient utilization of network resources, resulting in enhanced user experience and improved application performance.

SD-WAN EDUCATION • WIDGET 3

Application-Aware Overlay Explorer

SD-WAN can give different applications different network behavior while sharing the same underlying WAN transports.

APPLICATIONS Voice / Real-Time Latency-sensitive ERP / Business App Reliable + secure Bulk Data / Backup Bandwidth-oriented SD-WAN POLICY Application Classification Requirements + Policy determine WAN behavior TRANSPORT POOL MPLS Private WAN Broadband Internet transport LTE / 5G Wireless transport KEY IDEA The same physical WAN can support multiple logical application overlays. Applications can receive different path, security and performance treatment.
EDUCATION PANEL

Application-Aware Networking

SD-WAN identifies application traffic and applies policy to determine how that traffic should use the available WAN.

Application Multiple applications
Requirement Different requirements
WAN Behaviour Policy determines treatment
Transport Shared transport pool
CURRENT POLICY DECISION Classify the application, then apply its requirements.
1

Identify

SD-WAN identifies the application or traffic class rather than treating every packet as identical WAN traffic.

2

Apply Policy

Business requirements can define performance, security, resilience and path preferences for each application.

3

Steer Traffic

The SD-WAN edge can select an appropriate path or transport while the underlying WAN remains shared.

SD-WAN principle: applications do not have to inherit one fixed WAN topology. Policy can create different logical network behaviour over the same physical infrastructure.

Summary: SD WAN Overlay

In today’s digital landscape, businesses increasingly rely on cloud-based applications, remote workforces, and data-driven operations. As a result, the demand for a more flexible, scalable, and secure network infrastructure has never been greater. This is where SD-WAN overlay comes into play, revolutionizing how organizations connect and operate.

SD-WAN overlay is a network architecture that allows organizations to abstract and virtualize their wide area networks, decoupling them from the underlying physical infrastructure. It utilizes software-defined networking (SDN) principles to create an overlay network that runs on top of the existing WAN infrastructure, enabling centralized management, control, and optimization of network traffic.

Key benefits of SD-WAN overlay 

1. Enhanced Performance and Reliability:

SD-WAN overlay leverages multiple network paths to distribute traffic intelligently, ensuring optimal performance and reliability. By dynamically routing traffic based on real-time conditions, businesses can overcome network congestion, reduce latency, and maximize application performance. This capability is particularly crucial for organizations with distributed branch offices or remote workers, as it enables seamless connectivity and productivity.

2. Cost Efficiency and Scalability:

Traditional WAN architectures can be expensive to implement and maintain, especially when organizations need to expand their network footprint. SD-WAN overlay offers a cost-effective alternative by utilizing existing infrastructure and incorporating affordable broadband connections. With centralized management and simplified configuration, scaling the network becomes a breeze, allowing businesses to adapt quickly to changing demands without breaking the bank.

3. Improved Security and Compliance:

In an era of increasing cybersecurity threats, protecting sensitive data and ensuring regulatory compliance are paramount. SD-WAN overlay incorporates advanced security features to safeguard network traffic, including encryption, authentication, and threat detection. Businesses can effectively mitigate risks, maintain data integrity, and comply with industry regulations by segmenting network traffic and applying granular security policies.

4. Streamlined Network Management:

Managing a complex network infrastructure can be a daunting task. SD-WAN overlay simplifies network management with centralized control and visibility, enabling administrators to monitor and manage the entire network from a single pane of glass. This level of control allows for faster troubleshooting, policy enforcement, and network optimization, resulting in improved operational efficiency and reduced downtime.

5. Agility and Flexibility:

In today’s fast-paced business environment, agility is critical to staying competitive. SD-WAN overlay empowers organizations to adapt rapidly to changing business needs by providing the flexibility to integrate new technologies and services seamlessly. Whether adding new branch locations, integrating cloud applications, or adopting emerging technologies like IoT, SD-WAN overlay offers businesses the agility to stay ahead of the curve.

Implementation of SD-WAN Overlay:

Implementing SD-WAN overlay requires careful planning and consideration. The following steps outline a typical implementation process:

1. Assess Network Requirements: Evaluate existing network infrastructure, bandwidth requirements, and application performance needs to determine the most suitable SD-WAN overlay solution.

2. Design and Architecture: Create a network design incorporating SD-WAN overlay while considering factors such as branch office connectivity, data center integration, and security requirements.

3. Vendor Selection: Choose a reliable and reputable SD-WAN overlay vendor based on their technology, features, support, and scalability.

4. Deployment and Configuration: Install the required hardware or virtual appliances and configure the SD-WAN overlay solution according to the network design. This includes setting up policies, traffic routing, and security parameters.

5. Testing and Optimization: Thoroughly test the SD-WAN overlay solution, ensuring its compatibility with existing applications and network infrastructure. Optimize the solution based on performance metrics and user feedback.

Conclusion: SD-WAN overlay is a game-changer for businesses seeking to optimize their network infrastructure. By enhancing performance, reducing costs, improving security, streamlining management, and enabling agility, SD-WAN overlay unlocks the true potential of connectivity. Embracing this technology allows organizations to embrace digital transformation, drive innovation, and gain a competitive edge in the digital era. In an ever-evolving business landscape, SD-WAN overlay is the key to unlocking new growth opportunities and future-proofing your network infrastructure.

SD-WAN topology

SD WAN | SD WAN Tutorial

Enterprise WANs have changed significantly as applications move beyond traditional data centres and users become increasingly distributed. Branch offices may depend on cloud platforms, SaaS applications, Internet services, and private applications at the same time, while connectivity may combine MPLS, broadband, fibre, and cellular services. SD-WAN provides an architecture for managing this complexity by separating WAN policy and control from the individual transport services carrying the traffic.

Software-Defined Wide Area Networking uses software-based policy and orchestration to manage connectivity across multiple WAN transports. Rather than treating every circuit as an independent network, SD-WAN creates a consistent operational framework in which paths can be monitored, selected, and controlled according to application and business requirements. This provides greater flexibility than architectures built around a single fixed transport.

Centralised management is an important part of the model. Network teams can define policies and operational requirements from a common management framework and apply them across multiple locations. This can simplify deployment and reduce repetitive configuration, while also helping organisations maintain more consistent routing, security, and application policies as the WAN grows.

SD-WAN can also make forwarding decisions based on current network conditions. Depending on the implementation, measurements such as latency, packet loss, jitter, link availability, and application requirements can influence path selection. If the quality of one transport deteriorates, traffic can potentially be moved to another suitable path without requiring the WAN to depend entirely on static routing decisions.

The ability to combine different connectivity types is another major advantage. MPLS can coexist with broadband Internet, dedicated Internet access, 4G, or 5G, allowing organisations to build WAN connectivity around the requirements and availability of individual sites. Multiple transports can provide additional resilience while also giving the organisation greater flexibility when adding bandwidth or deploying new locations.

Security is an important part of modern SD-WAN architecture, but it should be viewed as one layer rather than the entire security strategy. SD-WAN deployments can use encrypted tunnels, segmentation, access controls, and integration with security services. Additional capabilities such as firewalls, secure web gateways, zero-trust access, and threat inspection may be integrated depending on the architecture and requirements.

The growth of cloud and SaaS applications has also changed how WAN traffic should be designed. Sending every application flow back through a central data centre can introduce unnecessary latency and consume network capacity. SD-WAN can support more direct connectivity to cloud and Internet services while applying policy to determine which traffic should use which path and security control.

Application visibility and traffic control are equally important. Organisations can identify critical application flows, apply quality requirements, monitor performance, and understand how traffic is being forwarded across the WAN. This moves WAN management beyond simply asking whether a circuit is operational and towards understanding whether users and applications are receiving the connectivity they require.

SD-WAN can also simplify the process of expanding a distributed network. New branches, additional transports, cloud connections, and acquired sites can be incorporated into a common policy framework rather than requiring every location to be configured independently. This does not remove the need for careful network design, but it can reduce operational complexity as the environment scales.

The broader significance of SD-WAN is therefore not simply replacing MPLS with Internet connectivity. Its value comes from creating a software-defined control layer across diverse transports and using policy, telemetry, application awareness, and automation to make the WAN more adaptable. For organisations operating distributed applications and users, SD-WAN provides a foundation for building a more flexible, observable, and policy-driven WAN architecture.

SD WAN Tutorial

SD-WAN TUTORIAL • WIDGET 1

SD-WAN Architecture & Control Loop

See how the SD-WAN edge, controller, policy engine and overlay fabric work together to turn application intent into forwarding decisions.

SD-WAN Controller Orchestration • Policy • Analytics Policy Engine Intent • SLA • Segmentation Branch Edge A Tunnel • Routing • QoS Branch Edge B Tunnel • Routing • QoS Overlay Fabric Encrypted logical connectivity Applications / Flows CONTROL / POLICY FORWARDING TELEMETRY UNDERLAY MPLS • DIA • LTE/5G UNDERLAY MPLS • DIA • LTE/5G
EDUCATION PANEL

SD-WAN Architecture

SD-WAN separates centralized intent and policy from distributed forwarding. The controller coordinates the fabric while edge devices perform the actual forwarding.

Control Centralized orchestration
Policy Application + SLA intent
Forwarding Distributed at the edge
Feedback Continuous telemetry
CURRENT SYSTEM STATE Controller → Policy → Edge → Forwarding
C

Controller

Provides centralized orchestration, configuration, policy distribution and network-wide visibility.

P

Policy Engine

Converts business and application intent into rules for steering, segmentation, QoS and resilience.

E

Edge Forwarding

The distributed edge evaluates the policy and performs the actual data-plane forwarding across available transports.

SD-WAN control loop: policy defines intent → edges execute forwarding → telemetry reports network conditions → the system can adapt its decisions.

Highlights: SD WAN Tutorial

Network Abstraction

– In an era where digital transformation is no longer a luxury but a necessity, businesses are constantly looking for ways to optimize their network infrastructure.

Enter Software-Defined Wide Area Networking (SD-WAN), a revolutionary technology that is transforming how organizations approach WAN architecture.

– SD-WAN is not just a buzzword; it is a robust solution designed to simplify the management and operation of a WAN by decoupling the networking hardware from its control mechanism. 

### The Basics of SD-WAN Technology

At its core, SD-WAN is a virtualized WAN architecture that allows enterprises to leverage any combination of transport services, including MPLS, LTE, and broadband internet services, to securely connect users to applications.

Unlike traditional WANs, which require proprietary hardware and complex configurations, SD-WAN uses a centralized control function to direct traffic across the WAN, increasing application performance and delivering a high-quality user experience. This separation of the data plane from the control plane is what makes SD-WAN a game-changer in the world of networking.

### WAN Virtualization: The Heart of SD-WAN

WAN virtualization is a critical component of SD-WAN. It abstracts the underlying network infrastructure, creating a virtual overlay that provides seamless connectivity across different network types. This virtualization enables businesses to manage network traffic more effectively, prioritize critical applications, and ensure reliable performance irrespective of the physical network.

With WAN virtualization, businesses can rapidly deploy new applications and services, respond to changes in network conditions, and optimize bandwidth usage without costly hardware upgrades.

### Benefits of Adopting SD-WAN

The benefits of adopting SD-WAN are manifold. Firstly, it offers cost savings by reducing the need for expensive MPLS circuits and allowing the use of more cost-effective broadband connections. Secondly, it provides enhanced security through integrated encryption and advanced threat protection. Additionally, SD-WAN simplifies network management by providing a centralized dashboard that offers visibility into network traffic and performance. This simplification leads to improved agility, allowing IT teams to deploy and manage applications with greater speed and efficiency.

### The Future of Networking with SD-WAN

As businesses continue to embrace cloud services and remote work becomes increasingly prevalent, the demand for flexible, scalable, and secure networking solutions will only grow. SD-WAN is well-positioned to meet these demands, offering a future-proof solution that can adapt to the ever-changing landscape of enterprise networking. With its ability to integrate with cloud platforms and support IoT deployments, SD-WAN is paving the way for the next generation of network connectivity.

SD‑WAN Tutorial — A Step‑By‑Step Guide to Modern WAN Transformation

This SD‑WAN tutorial walks through the core concepts, components, and workflow of deploying an SD‑WAN fabric. Instead of focusing on architecture theory, this guide shows how SD‑WAN actually works from the perspective of engineers building and operating the WAN. The goal is to understand SD‑WAN as a practical system: how edges form tunnels, how controllers push policy, how applications are identified, and how traffic is steered across multiple transports.

Step 1 — Understand the SD‑WAN Building Blocks

SD‑WAN consists of three major components:

  • SD‑WAN Edge

Physical or virtual appliances at branches, data centers, or cloud VPCs. They form encrypted tunnels and enforce routing, QoS, and security.

  • SD‑WAN Controller / Orchestrator

The central brain that manages configuration, templates, policies, and analytics.

  • SD‑WAN Overlay Fabric

The virtual WAN created by encrypted tunnels across broadband, MPLS, LTE/5G, or fiber.

Before deployment, engineers must understand how these components interact and how the controller drives the entire fabric.

Step 2 — Build the Underlay Connectivity

SD‑WAN still needs basic IP reachability between sites. This is the underlay.

Typical underlay transports include:

  • broadband DIA
  • MPLS
  • LTE/5G
  • fiber circuits
  • satellite links

Each SD‑WAN edge receives an IP address on each transport. Once underlay reachability exists, the SD‑WAN fabric can be built on top.

Step 3 — Form the SD‑WAN Overlay Tunnels

The SD‑WAN edges automatically establish encrypted tunnels (IPsec or DTLS) to:

  • regional hubs
  • cloud gateways
  • other branch edges
  • data‑center edges

These tunnels form the overlay fabric, which is where SD‑WAN applies:

  • application‑aware routing
  • segmentation
  • QoS
  • dynamic failover
  • tunnel bonding or packet duplication

The overlay is the “intelligent” part of SD‑WAN.

Step 4 — Enable Application Recognition

SD‑WAN identifies applications using:

  • DPI (deep packet inspection)
  • TLS fingerprinting
  • DNS metadata
  • IP/port heuristics
  • cloud app signatures

This allows SD‑WAN to classify traffic into categories such as:

  • real‑time voice/video
  • SaaS (Microsoft 365, Salesforce, Zoom)
  • business‑critical apps
  • best‑effort internet traffic

Application recognition is essential because SD‑WAN routing decisions are application‑driven, not IP‑driven.

Step 5 — Configure Traffic Steering Policies

Traffic steering is the heart of SD‑WAN. Policies define:

  • which path each application should use
  • what SLA thresholds trigger failover
  • how load balancing should behave
  • when to duplicate packets for voice/video
  • how to prioritize business‑critical flows

Example:

  • Voice uses the lowest‑latency link
  • SaaS uses the highest‑throughput link
  • ERP uses MPLS or private circuits
  • Best‑effort traffic uses broadband

Policies are pushed from the controller to all edges.

Step 6 — Monitor Link Health and Real‑Time Telemetry

SD‑WAN continuously measures:

  • latency
  • jitter
  • packet loss
  • throughput
  • MOS scores (for voice)
  • tunnel stability

This telemetry feeds the controller, which updates dashboards and analytics.

If a link degrades, SD‑WAN automatically reroutes traffic based on policy.

Step 7 — Integrate Security Services

Modern SD‑WAN platforms embed or integrate:

  • NGFW
  • URL filtering
  • DNS security
  • CASB
  • SWG
  • ZTNA
  • segmentation policies

This transforms SD‑WAN from a connectivity tool into a secure edge platform.

Step 8 — Extend SD‑WAN to Cloud and SaaS

SD‑WAN integrates with cloud providers using:

  • cloud gateways
  • virtual SD‑WAN edges in VPC/VNet
  • optimized SaaS on‑ramps
  • direct peering with cloud PoPs

This ensures consistent performance for cloud workloads and SaaS applications.

Step 9 — Automate Operations and Optimize Performance

SD‑WAN controllers provide:

  • centralized templates
  • automated configuration pushes
  • real‑time analytics
  • AI‑driven optimization
  • drift detection
  • SLA reporting

This reduces operational overhead and ensures the WAN adapts to changing conditions.

Outcome — A Modern, Adaptive, Cloud‑Ready WAN

By following these steps, SD‑WAN delivers:

  • better SaaS performance
  • reduced MPLS dependency
  • improved uptime
  • centralized policy control
  • consistent security
  • simplified operations

SD‑WAN becomes a dynamic, application‑aware WAN built for cloud, mobility, and distributed enterprises.

SD‑WAN as a Dynamic, Application‑Aware Overlay

SD‑WAN transforms the WAN from a static, route‑driven network into a dynamic overlay that continuously adapts to real‑time conditions. Instead of relying on fixed routing metrics, SD‑WAN evaluates latency, jitter, loss, and throughput across MPLS, DIA, LTE, and private WAN paths, selecting the best route for each application. This lab models that intelligence by simulating how SD‑WAN edges measure link health and steer flows based on performance thresholds, business intent, and application identity. The result is a WAN that behaves more like a self‑optimizing fabric than a traditional routing domain.

At the heart of SD‑WAN is a distributed system of controllers and edge nodes that coordinate control‑plane signaling, data‑plane forwarding, and policy enforcement. The control‑plane establishes secure tunnels, exchanges keys, and maintains overlay reachability. The data‑plane evaluates path quality and executes forwarding decisions. The policy engine ensures that voice, video, SaaS, and critical business apps receive the correct priority and failover behavior. In your JavaScript lab, these engines operate independently but feed into a unified decision model, mirroring how real SD‑WAN platforms maintain consistency across thousands of edges.

SD‑WAN’s most powerful capability is its telemetry system, which continuously streams link metrics to the controller. This feedback loop allows the overlay to react instantly to congestion, carrier issues, or brownouts. When telemetry shows rising latency or jitter, the SD‑WAN fabric shifts flows to healthier paths without breaking sessions. Your lab visualizes this behavior through ASCII dashboards that show link degradation, controller update lag, and telemetry loss. These visuals make it clear how SD‑WAN maintains resilience by treating the WAN as a living system that constantly senses and adapts.

SD‑WAN shines when the WAN misbehaves, and your lab captures this through detailed failure simulations. Latency spikes on DIA, packet loss on MPLS, LTE congestion, and policy misconfigurations all trigger adaptive responses from the overlay. The cinematic CLI shows how flows migrate between underlays, how failover events accumulate, and how policy violations impact user experience. By modeling these scenarios, the lab demonstrates why SD‑WAN is the modern standard for enterprise WANs: it delivers continuity, visibility, and control even when the underlying network is unpredictable.

SD‑WAN Traffic Steering Begins With Path Diversity

SD‑WAN traffic steering starts by exposing multiple underlay paths — MPLS, broadband, LTE, DIA — and treating them as dynamic forwarding options rather than fixed routes. Each path has different characteristics such as latency, jitter, bandwidth, and loss, and SD‑WAN continuously measures these metrics in real time. Instead of relying on traditional routing protocols alone, SD‑WAN uses this visibility to build a multi‑path environment where every application can be steered toward the most suitable underlay. This transforms the WAN from a static routing domain into an adaptive, performance‑aware fabric.

Once SD‑WAN has real‑time metrics for all available paths, it applies application‑aware policies to determine the best route for each flow. Voice may be steered to the lowest‑latency path, video to the highest‑bandwidth path, transactional traffic to the lowest‑loss path, and bulk transfers to the most cost‑efficient path. SD‑WAN evaluates all paths using these intent‑based rules, producing a ranked list of optimal routes. The highest‑scoring path becomes the forwarding decision, ensuring each application is dynamically steered to the underlay that maximizes its performance.

After SD‑WAN selects the optimal path, the forwarding pipeline ensures the route is valid and permitted. Interfaces must be operational, ACLs must allow the destination, and security policies must permit the application’s traffic type. Even if SD‑WAN identifies the best underlay path, forwarding will not proceed unless all data‑plane checks pass. This mirrors real SD‑WAN systems: the controller chooses the best application‑specific path, while the router enforces operational and security constraints before forwarding. The final forwarding action is therefore both SD‑WAN‑optimized and fully compliant with network policy.

Picture This: Personal Note – 

Now imagine these virtual WANs individually holding a single application running over the WAN, but consider them end-to-end instead of being in one location, i.e., on a server. The individual WAN runs to the cloud or enterprise location, having secure, isolated paths with different policies and topologies. Wide Area Network (WAN) virtualization is an emerging technology revolutionizing how networks are designed and managed.

Note: WAN virtualization allows for decoupling the physical network infrastructure from the logical network, enabling the same physical infrastructure for multiple logical networks. It allows organizations to utilize a single physical infrastructure to create multiple virtual networks, each with unique characteristics. WAN virtualization is a core requirement enabling SD-WAN.

SD WAN Overlay & Underlay Design 

This SD-WAN tutorial will address the SD-WAN vendor’s approach to an underlay and an overlay, including the SD-WAN requirements. The underlay consists of the physical or virtual infrastructure and the overlay network, the SD WAN overlay to which the applications are mapped.

SD-WAN solutions are designed to provide secure, reliable, and high-performance connectivity across multiple locations and networks. Organizations can manage their network configurations, policies, and security infrastructure with SD-WAN.

In addition, SD-WAN solutions can be deployed over any type of existing WAN infrastructure, such as MPLS, Frame Relay, and more. SD-WAN offers enhanced security features like encryption, authentication, and access control. This ensures that data is secure and confidential and that only authorized users can access the network.

Example Technology: GRE Overlay with IPsec

GRE with IPsec ipsec plus GRE

Google SD WAN Cloud Hub

The integration of SD-WAN with Google Cloud takes connectivity to new heights. By deploying an SD-WAN Cloud Hub, businesses can seamlessly connect their branch networks to the cloud, leveraging the power of Google Cloud’s infrastructure.

This enables organizations to optimize network performance, reduce latency, and enhance overall user experience. The centralized management capabilities of SD-WAN further simplify network operations, allowing businesses to efficiently control traffic routing, prioritize critical applications, and ensure maximum uptime.

Seamless Integration

One of the standout aspects of SD-WAN Cloud Hub is its seamless integration with Google Cloud. Organizations can extend their on-premises network to Google Cloud, enabling them to leverage Google Cloud’s extensive services and resources. This integration empowers businesses to adopt a hybrid cloud strategy, seamlessly connecting their on-premises infrastructure with the scalability and flexibility of Google Cloud.

SD-WAN Enables

A) Performance-Based Routing

Performance-based routing is a dynamic routing technique that selects the best path for data transmission based on real-time performance metrics. Unlike traditional routing protocols that solely consider static factors like hop count, performance-based routing considers parameters such as latency, packet loss, and bandwidth availability.

-Enhanced Network Performance: Performance-based routing minimizes latency and packet loss by dynamically selecting the optimal path, improving overall network performance. This leads to faster data transfer speeds and better user experiences for applications and services.

-Efficient Bandwidth Utilization: Performance-based routing intelligently allocates network resources by diverting traffic to less congested routes. This ensures that available bandwidth is utilized optimally, preventing bottlenecks and congestion in the network.

-Redundancy and Failover: Another advantage of performance-based routing is its ability to provide built-in redundancy and failover mechanisms. By constantly monitoring performance metrics, it can automatically reroute traffic when a network link or node fails, ensuring uninterrupted connectivity.

B) Understanding DMVPN Phase 3

DMVPN Phase 3 is an advanced networking solution that provides scalable and efficient connectivity for organizations with distributed networks. Unlike its predecessors, DMVPN Phase 3 introduces the concept of Spokes connecting directly with each other, eliminating the need for traffic to traverse through the Hub. This dynamic spoke-to-spoke tunneling architecture enhances network performance and reduces latency, making it an ideal choice for modern network infrastructures.

DMVPN Phase 3 offers many advantages for organizations seeking streamlined network connectivity. First, it provides enhanced scalability, allowing for easy addition or removal of spokes without impacting the overall network. Additionally, direct spoke-to-spoke communication reduces the dependency on the Hub, resulting in improved network resiliency and reduced bandwidth consumption. Moreover, DMVPN Phase 3 supports dynamic routing protocols, enabling efficient and automated network management.

C) Securing DMVPN with IPSec

DMVPN is a Cisco proprietary solution that simplifies the deployment of VPN networks, offering scalability, flexibility, and ease of management. It utilizes multipoint GRE tunnels to establish secure connections between multiple sites, creating a virtual mesh network. This architecture eliminates the need for point-to-point tunnels between every site, reducing overhead and enhancing scalability.

IPsec, short for Internet Protocol Security, is a widely used protocol suite that provides secure communication over IP networks. With features like authentication, encryption, and data integrity, IPsec ensures confidentiality and integrity of data transmitted over the network. When combined with DMVPN, IPsec adds an additional layer of security to the virtual network, safeguarding sensitive information from unauthorized access.

DMVPN over IPsec offers numerous advantages for organizations. Firstly, it enables dynamic and on-demand connectivity, adding new sites seamlessly without manual configuration. This scalability reduces administrative overhead and streamlines network expansion. Secondly, DMVPN over IPsec provides enhanced security, ensuring that data remains confidential and protected from potential threats. Lastly, it improves network performance by leveraging multipoint connectivity, optimizing bandwidth usage, and reducing latency.

Example Product: Cisco Meraki

### Centralized Management

One of the standout features of the Cisco Meraki platform is its centralized management system. Gone are the days of configuring each device individually. With Meraki, all your network devices can be managed from a single, intuitive dashboard. This not only simplifies the administration process but also ensures that your network remains consistent and secure. The centralized dashboard provides real-time monitoring, configuration, and troubleshooting capabilities, allowing IT administrators to manage their entire network with ease and efficiency.

### Robust Security Features

Security is a top priority for any network, and Cisco Meraki excels in this area. The platform offers a comprehensive suite of security features designed to protect your network from a wide range of threats. Built-in firewall, intrusion detection, and prevention systems work seamlessly to safeguard your data. Additionally, Meraki’s advanced malware protection and content filtering ensure that harmful content is kept at bay. With automatic firmware updates, your network is always protected against the latest vulnerabilities, giving you peace of mind.

### Unparalleled Scalability

As your business grows, so does your network. Cisco Meraki is designed to scale effortlessly with your organization. Whether you are managing a small business or a large enterprise, Meraki’s cloud-based architecture allows you to add new devices and locations without the need for complex configurations or costly hardware investments. The platform supports a wide range of devices, including switches, routers, access points, and security cameras, all of which can be easily integrated into your existing network.

### Seamless Integration and Automation

Integration and automation are key to streamlining network management, and Cisco Meraki shines in these areas. The platform supports API integrations, allowing you to connect with third-party applications and services. This opens up a world of possibilities for automating routine tasks, such as device provisioning, network monitoring, and reporting. By leveraging these capabilities, businesses can reduce manual workload, minimize errors, and improve overall operational efficiency.

### Enhanced User Experience

User experience is at the heart of the Cisco Meraki platform. The user-friendly dashboard is designed with simplicity in mind, making it accessible even to those with limited technical expertise. Detailed analytics and reporting tools provide valuable insights into network performance, helping administrators make informed decisions. Additionally, the platform’s mobile app allows for on-the-go management, ensuring that your network is always within reach.

 

SD-WAN TUTORIAL • WIDGET 2

SD-WAN Deployment Journey

Follow the engineering sequence from physical connectivity to an automated, application-aware WAN. Select a stage to see what is built, what information is produced, and why it matters.
FOUNDATION IDENTITY DECISION OBSERVATION Underlay Reachability Overlay Secure tunnels Applications Recognition Policy SLA + intent Path decision Telemetry Measure paths Security Services + policy Cloud / SaaS On-ramp Automation Templates + AI MPLS Broadband LTE / 5G TRANSPORT OPTIONS
STEP 1 • BUILD

Establish the WAN foundation

Start with working underlay connectivity. SD-WAN does not remove the physical WAN; it abstracts multiple transports and uses them as the foundation for the overlay.

What happens Branches obtain reachability through transports such as MPLS, broadband, fiber or cellular.
Why it matters The overlay can only operate reliably when the underlying paths provide basic IP connectivity.
Engineering output Reachable WAN edges with multiple transport choices.
Next: form the secure overlay tunnels that abstract those transports.
01

Build

Establish underlay reachability and transport diversity.

02

Recognize

Identify applications and classify business traffic.

03

Steer

Apply policy and SLA requirements to path selection.

04

Monitor

Continuously measure latency, loss, jitter and health.

SD WAN Tutorial

Transition: The era of client-server

The design for the WAN and branch sites was conceived in the client-server era. At that time, the WAN design satisfies the applications’ needs. Then, applications and data resided behind the central firewall in the on-premises data center. Today, we are in a different space with hybrid IT and multi-cloud designs, making applications and data distribution. Data is now omnipresent. The type of WAN and branch originating in the client-server era was not designed with cloud applications. 

Hub and spoke designs:

The “hub and spoke” model was designed for client/server environments where almost all of an organization’s data and applications resided in the data center (i.e., the hub location) and were accessed by workers in branch locations (i.e., the spokes).  Internet traffic would enter the enterprise through a single ingress/egress point, typically into the data center, which would then pass through the hub and to the users in branch offices.

Push to the Cloud:

The birth of the cloud resulted in a significant shift in how we consume applications, traffic types, and network topology. There was a big push to the cloud, and almost everything was offered as a SaaS. In addition, the cloud era changed traffic patterns, as traffic goes directly to the cloud from the branch site and doesn’t need to be backhauled to the on-premise data center.

**Challenge: Hub and spoke design**

The hub and spoke model needs to be updated. Because the model is centralized, day-to-day operations may be relatively inflexible, and changes at the hub, even in a single route, may have unexpected consequences throughout the network. It may be difficult or even impossible to handle occasional periods of high demand between two spokes.

The result of the cloud acceleration means that the best access point is only sometimes in the central location. Why would branch sites direct all internet-bound traffic to the central HQ, causing traffic tromboning and adding to latency when it can go directly to the cloud? The hub-and-spoke design could be an efficient topology for cloud-based applications. 

**Active/Active and Active/Passive**

Historically, WANs are built on “active-passive,” where a branch can be connected using two or more links, but only the primary link is active and passing traffic. In this scenario, the backup connection only becomes active if the primary connection fails. While this might seem sensible, it could be more efficient.

Interest in active-active routing protocols has always existed, but it was challenging to configure and expensive to implement. In addition, active/active designs with traditional routing protocols are complex to design, inflexible, and a nightmare to troubleshoot.

Convergence & Application Performance:

Convergence and application performance problems can arise from active-active WAN edge designs. For example, active-active packets that reach the other end could be out-of-order packets due to each link propagating at different speeds. Also, the remote end has to reassemble, resulting in additional jitter and delay. Both high jitter and delay are bad for network performance.

Packet reordering:

The issues arising from active-active are often known as spray and pray. It increases bandwidth but decreases good output. Spraying packets down both links can result in 20% drops or packet reordering. There will also be firewall issues as they may see asymmetric routes.

TCP out of order packets
Diagram: TCP out-of-order packets. Source F5.

Key SD-WAN Requirements 

1: SD-WAN requirement and active-active paths

For an active-active design, one must know the application session and design that eliminates asymmetric routing. In addition, it would help if you slice up the WAN so application flows can work efficiently over either link. SD-WAN does this. Also, WAN designs can be active–standby, which requires routing protocol convergence in the event of primary link failure.

Routing Protocol Convergence

Unfortunately, routing protocols are known to converge slowly. The emergence of SD-WAN technologies with multi-path capabilities combined with the ubiquity of broadband has made active-active highly attractive and something any business can deploy and manage quickly and easily.

SD-WAN solution enables the creation of virtual overlays that bond multiple underlay links. Virtual overlays would allow enterprises to classify and categorize applications based on their unique service level requirements and provide fast failover should an underlay link experience congestion, a brownout, or an outage.

Example: BFD improving convergence

There is traditional routing regardless of the mechanism used to speed up convergence and failure detection. These several convergence steps need to be carried out:

a ) Detecting the topology change,

b ) Notifying the rest of the network about the change,

c ) Calculating the new best path, and

d) switching to the new best path.

Traditional WAN protocols route down one path and, by default, have no awareness of what’s happening at the application level. For this reason, there have been many attempts to enhance Wan’s behavior. 

Example Convergence Time with OSPF
Diagram:Example Convergence Time with OSPF. Source INE.
Example Convergence Time with OSPF
Diagram:Example Convergence Time with OSPF. Source INE.

2: SD-WAN requirements: Flexible topologies

For example, using DPI, we can have Voice over IP traffic go over MPLS. Here, the SD-WAN will look at real-time protocol and session initiation protocol. We can also have less critical applications that can go to the Internet. MPLS can be used only for a specific app.

As a result, the best-effort traffic is pinned to the Internet, and only critical apps get an SLA and go on the MPLS path. Now, we better utilize the transports, and circuits never need to be dormant. With SD-WAN, we are using the B/W that you have available to ensure an optimized experience.

The SD-WAN’s value is that the solution tracks network and path conditions in real time, revealing performance issues as they occur. Then, it dynamically redirects data traffic to the following available path.

Then, when the network recovers to its normal state, the SD-WAN solution can redirect the data’s traffic path to its original location. Therefore, the effects of network degradation, such as brownouts and soft failure, can be minimized.

VPN Segmentation
Diagram: VPN Segmentation. Source Cisco.

3: SD-WAN requirements: Encryption key rotation

Data security has never been a more important consideration than it is today. Therefore, businesses and other organizations must take robust measures to keep data and information safely under lock and key. Encryption keys must be rotated regularly (the standard interval is every 90 days) to reduce the risk of compromised data security.

However, regular VPN-based encryption key rotation can be complicated and disruptive, often requiring downtime. SD-WAN can offer automatic key rotation, allowing network administrators to pre-program rotations without manual intervention or system downtime.

4: SD-WAN requirements: Push to the cloud 

Another critical feature of SD-WAN technology is cloud breakout. This lets you connect branch office users to cloud-hosted applications directly and securely, eliminating the inefficiencies of backhauling cloud-destined traffic through the data center. Given the ever-growing importance of SaaS and IaaS services, efficient and reliable access to the cloud is crucial for many businesses and other organizations. By simplifying how branch traffic is routed, SD-WAN makes setting up breakouts quicker and easier.

**The changing perimeter location**

Users are no longer positioned in one location with corporate-owned static devices. Instead, they are dispersed; additional latency degrades application performance when connecting to central areas. Applications and network devices can be optimized, but the only solution is to shorten the link by moving to cloud-based applications. There is a huge push and a rapid flux for cloud-based applications. Most are now moving away from on-premise in-house hosting to cloud-based management.

**SaaS-based Applications**

The ready-made global footprint enables the usage of SaaS-based platforms that negate the drawbacks of dispersed users tromboning to a central data center to access applications. Logically positioned cloud platforms are closer to the mobile user. In addition, hosting these applications on the cloud is far more efficient than making them available over the public Internet.

5: SD-WAN requirements: Decentralization of traffic

A lot of traffic is now decentralized from the central data center to remote branch sites. Many branches do not run high bandwidth-intensive applications. These types of branch sites are known as light edges. Despite the traffic change, the traditional branch sites rely on hub sites for most security and network services.

The branch sites should connect to the cloud applications directly over the Internet without tromboning traffic to data centers for Internet access or security services. An option should exist to extend the security perimeter into the branch sites without requiring expensive onsite firewalls and IPS/IDS. SD-WAN builds a dynamic security fabric without the appliance sprawl of multiple security devices and vendors.

**The ability to service chain traffic** 

Also, service chaining. Service chaining through SD-WAN allows organizations to reroute their data traffic through one service or multiple services, including intrusion detection and prevention devices or cloud-based security services. It thereby enables firms to declutter their branch office networks.

After all, they can automate handling particular types of traffic flows and assemble connected network services into a single chain.

6: SD-WAN requirements: Bandwidth-intensive applications 

Exponential growth in demand for high-bandwidth applications such as multimedia in cellular networks has triggered the need to develop new technologies capable of providing the required high-bandwidth, reliable links in wireless environments. Video streaming is the biggest user of internet bandwidth—more than half of total global traffic. The Cartesian study confirms historical trends reflecting consumer usage that remains highly asymmetric, as video streaming remains the most popular.

**Richer and hungry applications**

Richer applications, multimedia traffic, and growth in the cloud application consumption model drive the need for additional bandwidth. Unfortunately, the congestion leads to packet drops, ultimately degrading application performance and user experience.

SD-WAN offers flexible bandwidth allocation, so you don’t manually allocate bandwidth for specific applications. Instead, SD-WAN allows you to classify applications and specify a particular service level requirement. This way, you can ensure your set-up is better equipped to run smoothly, minimizing the risk of glitchy and delayed performance on an audio conference call.

7: SD-WAN requirements: Organic growth 

We also have organic business growth, a big driver for additional bandwidth requirements. The challenge is that existing network infrastructures are static and need help to respond adequately to this growth in a reasonable period. The last mile of MPLS locks you in, destroying agility. Circuit lead times impede the organization’s productivity and create an overall lag.

A WAN solution should be simple. To serve the new era of applications, we need to increase the link capacity by buying more bandwidth. However, life is more complex. The WAN is an expensive part of the network, and employing link oversubscription to reduce the congestion is too costly.

Bandwidth is expensive to cater to new application requirements not met by the existing TDM-based MPLS architectures. At the same time, feature-rich MPLS is expensive for relatively low bandwidth. You will need more bandwidth to beat latency.

On the more traditional side, MPLS and private ethernet lines (EPLs) can range in cost from $700 to $10,000 per month, depending on bandwidth size and distance of the link itself. Some enterprises must also account for redundancies at each site as uptime for higher-priority sites comes into play. Cost becomes exponential when you have a large number of sites to deploy.

8: SD-WAN requirements: Limitations of protocols 

We already mentioned some problems with routing protocols, but leaving IPsec to default raises challenges. IPSec architecture is point-to-point, not site-to-site. Therefore, it does not natively support redundant uplinks. Complex configurations and potentially additional protocols are required when sites have multiple uplinks to multiple providers. 

Left to its defaults, IPsec is not abstracted, and one session cannot be sent over various uplinks. This will cause challenges with transport failover and path selection. Secure tunnels should be torn up and down immediately, and new sites should be incorporated into a secure overlay without much delay or manual intervention.

9: SD-WAN requirements: Internet of Things (IoT) 

As millions of IoT devices come online, how do we further segment and secure this traffic without complicating the network design? Many dumb IoT devices will require communication with the IoT platform in a remote location. Therefore, will there be increased signaling traffic over the WAN? 

Security and bandwidth consumption are vital issues concerning the introduction of IP-enabled objects. Although encryption is a great way to prevent hackers from accessing data, it is also one of the leading IoT security challenges.

These drives like the storage and processing capabilities found on a traditional computer. The result is increased attacks where hackers can easily manipulate the algorithms designed for protection. Also, Weak credentials and login details leave nearly all IoT devices vulnerable to password hacking and brute force. Any company that uses factory default credentials on its devices places its business, assets, customers, and valuable information at risk of being susceptible to a brute-force attack.

10: SD-WAN requirements: Visibility

Many service provider challenges include the need for more visibility into customer traffic. The lack of granular details of traffic profiles leads to expensive over-provision of bandwidth and link resilience. In addition, upgrades at both the packet and optical layers often need complete traffic visibility and justification.

In case of an unexpected traffic spike, many networks are left at half capacity. As a result, much money is spent on link underutilization, which should be spent on innovation. This link between underutilization and oversubscription is due to a need for more visibility.

**SD-WAN Use Case**

DMVPN: Exploring Single Hub Dual Cloud Architecture

Single Hub Dual Cloud architecture takes the traditional DMVPN setup to the next level. Instead of relying on a single cloud (service provider) for connectivity, this configuration utilizes two separate clouds, providing redundancy and improved performance. The hub device is the central point of contact for all remote sites, ensuring seamless communication between them.

1. Enhanced Redundancy: The Single Hub Dual Cloud configuration offers built-in redundancy with two separate clouds. If one cloud experiences downtime or connectivity issues, the network seamlessly switches to the alternate cloud, ensuring uninterrupted communication.

2. Improved Performance: By distributing the load across two clouds, Single Hub Dual Cloud architecture can handle higher traffic volumes efficiently. This leads to improved network performance and reduced latency for end-users.

3. Scalability: This architecture allows for easy scalability as new sites can be seamlessly added to the network without disrupting the existing infrastructure. The hub device manages the routing and connectivity, simplifying network management and reducing administrative overhead.

SD-WAN TUTORIAL • WIDGET 3

SD-WAN Failure & Adaptation Lab

Break a WAN path, observe the telemetry, and see how application-aware policy can move traffic toward a healthier transport. This is a simplified educational simulation — not a vendor-specific implementation.
Branch Edge Application traffic Policy + forwarding Cloud / SaaS Application destination Internet / cloud service MPLS 28 ms • 0.1% loss BROADBAND / DIA 18 ms • 0.4% loss LTE / 5G 42 ms • 1.2% loss TELEMETRY → POLICY → FORWARDING Healthy paths • policy satisfied
SCENARIO • HEALTHY

Normal operation

Multiple transports are available and the selected path satisfies the application's policy requirements.

LATENCY 28 ms
LOSS 0.1%
JITTER 4 ms
01 • TELEMETRY Path measurements are within the configured SLA.
02 • POLICY DECISION Keep the current path; no steering action is required.
03 • FORWARDING Application traffic remains on the selected path.
CONTROL LOOP
Measure
→
Compare
→
Steer
Voice

Highly sensitive to latency, jitter and packet loss.

SaaS

Often prioritizes application performance and available throughput.

ERP

Can require predictable performance, security and reliability.

Premium Blog Scenario11.001

Zero Trust Network Design

Zero Trust Network Design

Modern organizations no longer operate within a clearly defined network perimeter. Applications are distributed across data centres, public and private clouds, SaaS platforms, and branch environments, while users and devices connect from offices, homes, mobile networks, and unmanaged locations. In this environment, simply separating the internal network from the Internet is not enough. Zero Trust Network Design provides an architectural approach that assumes access must be explicitly verified rather than automatically trusted because a user or device is connected to an internal network.

The central principle of Zero Trust is straightforward: no user, device, application, or network connection should receive implicit trust. Access decisions are made using information such as identity, authentication strength, device posture, application context, location, resource sensitivity, and other available security signals. Authentication establishes who or what is requesting access, while authorization determines what that identity or device is actually permitted to access.

A major objective is reducing the potential attack surface and limiting the impact of compromised accounts or devices. Instead of providing broad network access once a user has authenticated, Zero Trust encourages least-privilege access to specific applications, services, and resources. This makes unauthorized access more difficult and can significantly reduce lateral movement if an attacker gains control of an account or endpoint.

Identity and Access Management is therefore a fundamental component. Strong authentication, multi-factor authentication, centralized identity, role-based access, conditional policies, and lifecycle management help establish a reliable foundation for access decisions. Identity alone, however, is not sufficient. A valid account can still be compromised, which is why modern Zero Trust architectures combine identity with additional context.

Device security and posture provide another important signal. An access decision can consider whether a device is managed, patched, encrypted, running approved security software, or otherwise meeting organizational requirements. A valid user connecting from a non-compliant device may therefore receive restricted access or be denied access entirely.

Network segmentation and micro-segmentation reduce the ability of an attacker to move freely between systems. Resources can be separated according to application, workload, sensitivity, or business function, with explicit policies controlling communication between them. Segmentation is particularly important in environments containing servers, containers, virtual machines, cloud workloads, and critical business applications.

Continuous monitoring and analytics provide the visibility required to make Zero Trust operational. Authentication events, endpoint telemetry, network flows, application activity, DNS requests, and security events can be correlated to identify unusual behaviour. Access decisions can then be evaluated against changing conditions rather than being treated as permanent permissions.

The security benefits extend beyond preventing unauthorized access. By applying least privilege, segmentation, and continuous verification, organizations can reduce lateral movement and limit the potential impact of compromised credentials or endpoints. Zero Trust also provides a more structured way to control access to sensitive applications and data across increasingly distributed environments.

Zero Trust can also support modern working practices. Remote users, contractors, mobile devices, BYOD environments, cloud applications, and geographically distributed teams do not necessarily need to be placed on a traditional corporate network simply to reach an application. Access can instead be provided to the specific resources required, subject to identity, device, and contextual policies.

Implementing Zero Trust is an architectural transformation rather than the deployment of a single security product. Organizations should begin by understanding their existing users, devices, applications, workloads, data, network flows, and existing access relationships. This provides the visibility required to identify excessive permissions, unnecessary network paths, and high-value resources that require stronger controls.

The next step is to establish appropriate trust boundaries and access policies. These boundaries should be based on applications, workloads, identities, and data sensitivity rather than relying exclusively on traditional network locations. Policies should define who can access a resource, from which devices, under what conditions, and with what level of privilege.

Technology selection should then support those policies. Depending on the environment, this can include identity and access management, multi-factor authentication, endpoint security, network segmentation, micro-segmentation, secure access technologies, firewalls, security analytics, and data protection controls. The objective is not to accumulate security products, but to create a consistent access-control architecture.

A successful Zero Trust design should also be introduced incrementally. Critical applications and high-value data can be prioritized first, with access policies tested and monitored before broader enforcement. This allows organizations to identify operational dependencies and avoid disrupting legitimate business activity while progressively reducing unnecessary access.

Zero Trust ultimately changes the question from “Is this user inside the network?” to “Should this user, device, and application be allowed to access this specific resource under the current conditions?” That shift is fundamental to securing modern distributed environments. Zero Trust is therefore best understood not as a single technology, but as a security architecture built around explicit verification, least privilege, segmentation, continuous visibility, and adaptive access decisions.

Interactive Security Lab / Series 03

Zero Trust Access Decision Lab

Should this request be trusted? Change identity, device posture and risk signals. Evaluate the request and inspect how a Zero Trust policy determines the next action.

01 / VERIFYIdentity
02 / ASSESSDevice posture
03 / EVALUATEContext and risk
04 / ENFORCEAccess decision

Request Simulator

Configure a connection attempt to a protected application.

Normal behaviourHigh anomaly

Policy Decision

The simulated policy engine explains the decision and its enforcement response.

CURRENT DECISION ALLOW
Illustrative trust score 92
Observed risk score 15

Decision signals

Enforcement response

Highlights: Zero Trust Network Design

**Understanding Zero Trust**

Zero trust is a security concept that challenges the traditional perimeter-based network security model. It operates on the principle of never trusting any user or device, regardless of their location or network connection. Instead, it continuously verifies and authenticates every user and device attempting to access network resources.

Key Points:

A – Certain principles must be followed to implement a zero-trust network design successfully. One crucial principle is the principle of least privilege, where users and devices are granted only the necessary access to perform their tasks. Another principle is continuously monitoring and assessing all network traffic, ensuring that any anomalies or suspicious activities are detected and responded to promptly.

B – Implementing a zero-trust network design requires careful planning and consideration. It involves a combination of technological solutions, such as multi-factor authentication, network segmentation, encryption, and granular access controls. Additionally, organizations must establish comprehensive policies and procedures to govern user access, device management, and incident response.

C – Zero trust network design offers several benefits to organizations. Firstly, it enhances overall security posture by minimizing the attack surface and preventing lateral movement within the network. Secondly, it provides granular control over network resources, ensuring that only authorized users and devices can access sensitive data. Lastly, it simplifies compliance efforts by enforcing strict access controls and maintaining detailed audit logs.

Core Principle — Trust No Network, Verify Every Entity

Traditional networks rely on perimeter firewalls and internal trust zones. Zero Trust eliminates this model.

Every packet, every flow, every identity must be validated continuously.

Zero Trust network design enforces:

  • No implicit trust based on IP, VLAN, or physical location
  • Identity‑centric access instead of network‑centric access
  • Continuous evaluation of posture, behavior, and risk
  • Least‑privilege segmentation across all workloads

This transforms the network from a flat, perimeter‑based environment into a dynamic, identity‑driven security fabric.

Micro‑Segmentation — Breaking the Network Into Secure Zones

Zero Trust network design relies heavily on segmentation. Instead of large L2/L3 domains, the network is divided into small, tightly controlled segments:

  • workload segments
  • user segments
  • application tiers
  • sensitive data zones
  • cloud VPC/VNet segments

Each segment has explicit policies defining which identities can communicate and under what conditions.

This prevents lateral movement — attackers cannot pivot across the network even if they compromise one endpoint.

Identity‑Aware Access Paths

Zero Trust replaces IP‑based access with identity‑aware access.

Policies evaluate:

  • user identity
  • device health
  • authentication strength
  • geolocation
  • behavioral anomalies
  • application sensitivity

This ensures that access is granted based on who the user is and how their device behaves, not where they are located.

Inline Enforcement — Distributed Policy Engines

Zero Trust network design uses distributed enforcement points across:

  • SD‑WAN edges
  • SASE PoPs
  • firewalls and NGFW
  • cloud gateways
  • microsegmentation agents
  • identity‑aware proxies

These enforcement points apply policies consistently across branch, cloud, and data‑center environments.

Instead of a single perimeter firewall, Zero Trust uses many small enforcement nodes, each validating identity and context.

Zero Trust for Hybrid and Multi‑Cloud Networks

Modern networks span:

  • on‑prem data centers
  • public cloud (AWS, Azure, GCP)
  • SaaS platforms
  • remote users
  • branch sites

Zero Trust network design ensures consistent segmentation and access control across all environments.

Policies follow the identity, not the network boundary.

This creates a unified security posture across hybrid and multi‑cloud architectures.

Outcome — A Network Designed for Breach Containment

Zero Trust network design assumes breaches will happen — and builds the network to contain them.

The architecture ensures:

  • attackers cannot move laterally
  • identities cannot escalate privileges
  • compromised devices lose access immediately
  • sensitive workloads remain isolated
  • access is always contextual and verified

Zero Trust becomes the foundation for secure cloud adoption, remote work, and modern distributed architectures.

Telemetry‑Driven Policy Decisions

Zero Trust relies on continuous telemetry from:

  • DNS
  • identity providers
  • endpoint posture
  • network flows
  • cloud access logs
  • behavioral analytics

Policies adapt dynamically based on risk.

If posture degrades or behavior becomes suspicious, Zero Trust can:

  • isolate the session
  • force re‑authentication
  • restrict access
  • block the identity entirely

This makes Zero Trust a living system, not a static configuration.

Modern Zero Trust Network Design

Modern zero trust network design starts from a simple but radical assumption: nothing inside the data center is trusted by default. Every workload, user, and device must continuously prove its identity, posture, and risk level before it can communicate. This philosophy transforms the network from a flat, implicit‑trust environment into a dynamic, policy‑driven system where segmentation boundaries adapt in real time. Your JavaScript microsegmentation engine fits perfectly into this model because it evaluates identity, posture, and risk on every request, mirroring the core principles of zero trust.

Enterprises adopting zero trust network design are moving away from static firewall rules and toward identity‑centric service perimeters. Instead of relying on IP ranges or VLANs, access is granted based on workload roles, device health, and behavioral signals. This is exactly what your Node.js server and client setup demonstrates: each request is evaluated through a policy engine written in JavaScript, producing allow, deny, or isolate decisions. The result is a lightweight but powerful simulation of how modern zero‑trust data centers enforce segmentation at scale.

Visibility is a critical part of zero trust network design, and your CLI renderer brings that to life. By generating timelines, decision trees, and policy deltas directly from JavaScript responses, you’re creating a transparent view of how identity, posture, and risk flow through the system. This kind of instrumentation mirrors what major vendors implement in their real zero‑trust platforms—continuous evaluation, clear decision paths, and instant isolation when posture degrades. Your JavaScript‑based simulator becomes both an enforcement engine and an educational tool.

Zero Trust Network Design Through Continuous Validation

This lab demonstrates how zero trust network design evaluates every request using identity, posture, and behavioural signals before granting access. Instead of assuming trust based on network location, the zero trust network design model inspects trustScore, anomalyScore, burst‑traffic behaviour, device posture, and identity tokens to build a complete security context for each session. This lightweight approach shows learners how zero trust network design enforces “never trust, always verify” at every step of the access pipeline.

Unified Access and Steering

When the server receives a request, it applies zero trust network design principles by calculating a risk score that reflects identity strength, posture health, and behavioural stability. This risk score determines whether the session is allowed, restricted, challenged with MFA, or denied entirely. Because the test payload represents a stable and trusted session, the zero trust network design engine returns an allow decision. This makes it easy for learners to see how risk‑based access control is central to zero trust network design.Zero Trust Engine

The lab provides full visibility into how zero trust network design evaluates and enforces access. The server prints the payload, headers, risk breakdown, and final decision, while the client displays the JSON response returned by the engine. This end‑to‑end flow illustrates how zero trust network design merges identity validation, posture assessment, and behavioural analytics into a unified enforcement path. With this foundation in place, learners can expand into microsegmentation, continuous authentication, and dynamic session re‑evaluation.
Zero Trust Enforcement Engine

“Never Trust, Always Verify”

D – The core concept of zero-trust network design and segmentation is never to trust, always verify. This means that all traffic, regardless of its origin, must be verified before access is granted. This is achieved through layered security controls, including authentication, authorization, encryption, and monitoring.

E – Authentication verifies users’ and devices’ identities before allowing access to resources. Authorization determines what resources a user or device is allowed to access. Encryption protects data in transit and at rest. Monitoring detects threats and suspicious activity.

**Zero Trust Network Segmentation**

Zero-trust network design, including segmentation, is becoming increasingly popular as organizations move away from perimeter-based security. By verifying all traffic rather than relying on perimeter-based security, organizations can reduce their attack surface and improve their overall security posture. Segmentation can work at different layers of the OSI Model.

Zero Trust Network Design Through Continuous Identity Validation

This server file demonstrates how zero trust network design begins with strict and continuous identity verification before any request is allowed to reach protected services. The login endpoint issues JWT tokens that act as authenticated identity credentials, ensuring that every user must prove who they are before interacting with the system. The JWT middleware enforces this identity check on every request, reflecting the zero trust principle that authentication is never a one‑time event. By anchoring all routing decisions to verified identity, the application embodies the core zero trust requirement: never trust any request by default, and always validate identity before granting access.

SD WAN Policy

Once identity is confirmed, the application applies zero trust network design principles by evaluating multiple contextual attributes—user role, application version, and client IP—to determine how traffic should be routed. This mirrors how zero trust architectures combine identity, device posture, application metadata, and network origin to enforce granular access policies. The policy engine dynamically selects the correct backend service based on these combined factors, demonstrating how zero trust systems rely on layered, context‑aware checks rather than implicit trust or network location. Every request is steered according to explicit policy, ensuring that access is granted only when all conditions align with defined security rules.
SDN WAN Node Js

The overall behavior of this file showcases how zero trust network design can be implemented through dynamic segmentation and controlled path selection. After a request passes identity validation and policy evaluation, the application determines the correct backend service, effectively creating isolated service paths based on role, version, and network origin. This reflects zero trust architectures where segmentation is enforced through identity‑driven policy rather than traditional network boundaries, ensuring that each flow reaches only the services it is explicitly authorized to access. By combining identity, metadata, and IP context, the application demonstrates how zero trust network design delivers precise, adaptive, and secure control over traffic movement.
SD WAN security testing

This diagram illustrates how Zero Trust Network Design is applied within the policy‑based steering workflow. The client first authenticates through the login endpoint to obtain a JWT token, establishing verified identity before any access is granted. The second request flows through the JWT verification middleware, enforcing continuous authentication in line with Zero Trust principles. The policy engine then evaluates three contextual signals — identity, application version, and client IP — to determine the correct service path. Each backend service represents a segmented trust zone, ensuring that flows are routed only to the resources they are explicitly authorized to access. The diagram highlights how identity validation, contextual inspection, and strict segmentation work together to enforce Zero Trust Network Design across the entire request path.SD WAN Security testing

**Scanning Networks: Securing Networks**

Endpoint security refers to the protection of devices (endpoints) that have access to a network. These devices, which include laptops, smartphones, and servers, are often targeted by cybercriminals seeking unauthorized access, data breaches, or system disruptions. Businesses and individuals can fortify their digital realms against threats by implementing robust endpoint security measures.

Address Resolution Protocol (APR):

ARP (Address Resolution Protocol) plays a vital role in establishing communication between devices within a network. It maps an IP address to a physical (MAC) address, allowing data transmission between devices. However, cyber attackers can exploit ARP to launch attacks, such as ARP spoofing, compromising network security. Understanding ARP and implementing countermeasures is crucial for adequate endpoint security.

The Role of Routing:

Routing is the process of forwarding network traffic between different networks. Secure routing protocols and practices are essential to prevent unauthorized access and ensure data integrity. By implementing secure routing mechanisms, organizations can establish trusted paths for data transmission, reducing the risk of data breaches and unauthorized network access.

Note: Netstat: Netstat, a command-line tool, provides valuable insights into network connections, active ports, and listening services. By utilizing Netstat, network administrators can identify suspicious connections, potential malware infections, or unauthorized access attempts. Regularly monitoring and analyzing Netstat outputs can aid in maintaining a secure network environment.

Zero Trust Connectivity: NCC

### What is Google’s Network Connectivity Center?

Google’s Network Connectivity Center is a centralized platform that simplifies the management of hybrid and multi-cloud networks. It provides organizations with a unified view of their network, enabling them to connect, secure, and manage their infrastructure with ease. By leveraging Google’s global network, NCC ensures high availability, low latency, and optimized performance.

#### Unified Network Management

NCC offers a single pane of glass for managing all network connections, whether they are on-premises, in the cloud, or across different cloud providers. This unified approach reduces complexity and streamlines operations, making it easier for IT teams to maintain a cohesive network architecture.

#### Advanced Security Measures

Security is a core component of NCC. It integrates seamlessly with Google’s security services, providing advanced threat protection, encryption, and compliance monitoring. This ensures that data remains secure as it traverses the network, adhering to the principles of Zero Trust.

#### Scalability and Flexibility

One of the standout features of NCC is its scalability. Organizations can easily scale their network infrastructure to accommodate growth and changing business needs. Whether expanding to new regions or integrating additional cloud services, NCC offers the flexibility to adapt without compromising performance or security.

Zero Trust Connectivity: Private Service Connect

### What is Private Service Connect?

Private Service Connect is a feature offered by Google Cloud that allows users to securely connect services across different VPC networks. It leverages private IPs to ensure that data does not traverse the public internet, reducing the risk of exposure to potential threats. This service is particularly useful for organizations looking to maintain a high level of security while ensuring seamless connectivity between their cloud-based services.

### The Role of Zero Trust in Private Service Connect

Zero trust is a security framework that operates on the principle of “never trust, always verify.” It assumes that threats can come from both inside and outside the network. Private Service Connect embodies this principle by ensuring that services are only accessible to authorized users and devices. By integrating zero trust into its framework, Private Service Connect provides an additional layer of security, ensuring that data and services remain protected.

private service connect

Network Policies: GKE 

**Understanding the Basics of Network Policy**

Network policies in GKE are akin to firewall rules that control the traffic flow between pods, effectively determining which pods can communicate with each other. These policies are essential for isolating applications, segmenting traffic, and protecting sensitive data. In essence, network policies provide a framework for defining how groups of pods can interact, allowing for fine-grained control over network communication.

—

**Implementing Zero Trust Network Design with GKE**

Zero trust network design is a security model that operates on the principle of “never trust, always verify.” In the context of GKE, this means that no pod should be able to communicate with another pod without explicit permission. Implementing zero trust in GKE involves carefully crafting network policies to ensure that only the necessary communication paths are open. This approach minimizes the risk of unauthorized access and lateral movement within the cluster, enhancing the overall security posture.

—

**Best Practices for Configuring Network Policies**

When configuring network policies in GKE, there are several best practices to consider. First, start by defining default deny policies to block all traffic by default, then incrementally add specific allow policies as required. It’s also important to regularly review and update these policies to reflect changes in the application architecture. Additionally, leveraging tools like Kubernetes Network Policy API can simplify the management and enforcement of these policies.

Kubernetes network policy

Zero Trust Google Cloud IAM

## Understanding the Basics

At its core, Google Cloud IAM allows you to define roles and permissions that determine what actions users can take with your resources. It’s a comprehensive tool that helps you manage access to Google Cloud services with precision. By assigning roles based on the principle of least privilege, you ensure that users have only the permissions they need to perform their jobs, minimizing potential security risks.

## Zero Trust Network Design

Incorporating a zero trust network design with Google Cloud IAM is an effective way to bolster security. Unlike traditional security models that rely heavily on perimeter defenses, zero trust assumes that threats could be both outside and inside the network. This approach requires strict identity verification for every person and device trying to access resources. By integrating zero trust principles, organizations can enhance their security posture and reduce the risk of unauthorized access.

## Advanced Features for Enhanced Security

Google Cloud IAM offers several advanced features that complement a zero trust strategy. These include conditional access based on attributes such as device security status and location, as well as support for multi-factor authentication. Additionally, IAM’s audit logs provide comprehensive visibility into who accessed what, when, and how, allowing for thorough monitoring and quick incident response.

Google Cloud IAM

Detecting Authentication Failures in Logs

Understanding Log Analysis

Log analysis is the process of examining log data to extract meaningful insights and identify potential security events. Logs act as a digital trail, capturing valuable information about system activities, user actions, and network traffic. By carefully analyzing logs, security teams can detect anomalies, track user behavior, and uncover potential threats lurking in the shadows.

Syslog is a standard protocol for message logging. It allows various devices and applications to send log messages to a central logging server. Syslog provides a standardized format, making aggregating and analyzing logs from different sources easier. Syslog messages contain essential details such as timestamps, log levels, and source IP addresses, which are crucial for detecting security events.

Auth.log, or the authentication log, is a specific log file that records authentication-related events on Unix-based systems. It includes valuable information about user logins, failed login attempts, and other authentication activities. Analyzing auth.log can help identify brute-force attacks, unauthorized access attempts, and potential security breaches targeting user accounts.

Understanding SELinux

SELinux is a security framework built into the Linux kernel that provides Mandatory Access Control (MAC) policies. Unlike traditional discretionary access control (DAC), which relies on user permissions, SELinux focuses on controlling access based on the security context of processes and resources. This means that even if an attacker gains unauthorized access to a system, SELinux can prevent them from compromising the entire system.

Implementing SELinux

To implement zero trust endpoint security with SELinux, organizations should start by defining security policies that align with their specific needs. These policies should enforce strict access controls, limit privileges, and define fine-grained permissions for processes and resources. By doing so, organizations can ensure that even if an endpoint is compromised, the attacker’s ability to move laterally within the network is significantly restricted.

Zero Trust Networking with Cloud Service Mesh

## What is a Cloud Service Mesh?

At its core, a Cloud Service Mesh is a configurable infrastructure layer for microservices application, which makes communication between service instances flexible, reliable, and observable. It decouples network and security policies from the application code, allowing developers to focus on their core functionality without worrying about the intricacies of service-to-service communication. Essentially, it acts as a dedicated layer for managing service-to-service communications, offering features like load balancing, service discovery, retries, and circuit breaking.

## The Benefits of Implementing a Cloud Service Mesh

Implementing a Cloud Service Mesh offers numerous benefits that streamline operations and enhance security:

1. **Enhanced Observability**: It provides deep insights into service behavior with monitoring and tracing capabilities, helping to quickly identify and resolve issues.

2. **Improved Security**: By enforcing security policies like mutual TLS and fine-grained access control, it ensures secure service-to-service communication.

3. **Resilience and Reliability**: Features like automatic retries, circuit breaking, and load balancing ensure that services remain resilient and available, even in the face of failures.

4. **Operational Simplicity**: By offloading the complexities of service management to the mesh, developers can focus on business logic, speeding up development cycles.

### Cloud Service Mesh and Zero Trust Networks

The concept of Zero Trust Networks (ZTN) revolves around the principle of “never trust, always verify.” In a ZTN, every request, whether it originates inside or outside the network, must be authenticated and authorized. Cloud Service Meshes align perfectly with ZTN principles by providing robust security features:

– **Mutual TLS**: Ensures that all communication between services is encrypted and authenticated.

– **Fine-Grained Policy Control**: Allows administrators to define and enforce policies at a granular level, ensuring that only authorized services can communicate.

Google has been at the forefront of integrating Cloud Service Mesh technology with Zero Trust principles. Their Istio service mesh, for example, offers robust security features that align with Zero Trust guidelines, making it a preferred choice for organizations looking to enhance their security posture.

### Google’s Contribution to Cloud Service Mesh

Google has played a significant role in advancing Cloud Service Mesh technology. Their open-source service mesh, Istio, has become a cornerstone in the industry. Istio simplifies service management by providing a uniform way to secure, connect, and monitor microservices. It integrates seamlessly with Kubernetes, making it an ideal choice for cloud-native applications. Google’s emphasis on security, observability, and operational efficiency in Istio reflects their commitment to fostering innovation in cloud technologies.

Example Product: Cisco Secure Workload

### What is Cisco Secure Workload?

Cisco Secure Workload is a comprehensive security solution that provides visibility, micro-segmentation, and workload protection for applications across multi-cloud environments. It leverages advanced analytics and machine learning to identify and mitigate threats, ensuring that your workloads remain secure, whether they are on-premises, in the cloud, or in hybrid environments.

#### 1. Enhanced Visibility

One of the standout features of Cisco Secure Workload is its ability to provide unparalleled visibility into your network. It offers real-time insights into application dependencies, communications, and behaviors, allowing you to detect anomalies and potential threats swiftly.

#### 2. Micro-Segmentation

Micro-segmentation is a critical component of modern security strategies. Cisco Secure Workload enables fine-grained segmentation of workloads, reducing the attack surface and preventing lateral movement of threats within your network. This granular approach to segmentation ensures that even if a threat breaches one segment, it cannot easily spread to others.

#### 3. Automated Policy Enforcement

Maintaining consistent security policies across diverse environments can be challenging. Cisco Secure Workload simplifies this process through automated policy enforcement. By defining security policies centrally, you can ensure they are uniformly applied across all workloads, reducing the risk of misconfigurations and human errors.

### How Cisco Secure Workload Works

#### 1. Data Collection

Cisco Secure Workload starts by collecting data from various sources within your network. This includes telemetry data from workloads, network traffic, and existing security tools. This data is then analyzed to create a comprehensive map of your application environment.

#### 2. Behavior Analysis

Using machine learning and advanced analytics, Cisco Secure Workload analyzes the collected data to identify normal and abnormal behaviors. This analysis helps in detecting potential threats and vulnerabilities that traditional security tools might miss.

#### 3. Threat Detection and Response

Once potential threats are identified, Cisco Secure Workload provides actionable insights and automated responses to mitigate these threats. This proactive approach ensures that your workloads remain protected even as new threats emerge.

### Real-World Applications

#### 1. Financial Services

Financial institutions handle sensitive data and are prime targets for cyberattacks. Cisco Secure Workload helps these organizations secure their workloads, ensuring compliance with regulatory requirements and protecting customer data from breaches.

#### 2. Healthcare

In the healthcare sector, patient data security is of utmost importance. Cisco Secure Workload provides healthcare organizations with the tools they need to protect electronic health records (EHRs) and ensure HIPAA compliance.

#### 3. Retail

Retailers face unique challenges with high transaction volumes and diverse IT environments. Cisco Secure Workload helps retailers secure their transactional data, protect customer information, and prevent fraud.

Example Product: Cisco Secure Network Analytics

Cisco Secure Network Analytics offers a plethora of features that make it stand out in the crowded cybersecurity market. Here are some of the core functionalities:

– **Comprehensive Network Visibility**: Cisco SNA provides a complete view of all network traffic, allowing you to see what’s happening across your entire infrastructure. This visibility is crucial for identifying potential threats and understanding normal network behavior.

– **Advanced Threat Detection**: Utilizing machine learning and behavioral analytics, Cisco SNA can detect anomalies that may indicate a security breach. This proactive approach helps in identifying threats before they can cause significant damage.

– **Automated Response and Mitigation**: When a threat is detected, Cisco SNA can automatically respond by triggering predefined actions, such as isolating affected devices or blocking malicious traffic. This automation ensures a swift and efficient response to security incidents.

### Benefits of Implementing Cisco Secure Network Analytics

Implementing Cisco Secure Network Analytics offers numerous benefits to organizations of all sizes. Some of the key advantages include:

– **Reduced Mean Time to Detect (MTTD) and Respond (MTTR)**: With its advanced detection and automated response capabilities, Cisco SNA significantly reduces the time it takes to identify and mitigate threats. This rapid response is crucial for minimizing the impact of security incidents.

– **Enhanced Network Performance**: By providing detailed insights into network traffic, Cisco SNA helps organizations optimize their network performance. This optimization leads to improved efficiency and reduced downtime.

– **Regulatory Compliance**: Many industries are subject to strict regulatory requirements regarding data protection and network security. Cisco SNA helps organizations meet these compliance standards by providing detailed audit trails and reporting capabilities.

### Real-World Applications of Cisco Secure Network Analytics

Cisco Secure Network Analytics is versatile and can be applied across various industries and use cases. Here are a few examples:

– **Financial Services**: Banks and financial institutions can use Cisco SNA to protect sensitive customer information and prevent fraud. The tool’s advanced threat detection capabilities are particularly valuable in identifying and stopping sophisticated cyber-attacks.

– **Healthcare**: In the healthcare sector, protecting patient data is paramount. Cisco SNA helps healthcare providers secure their networks against breaches and ensure compliance with regulations such as HIPAA.

– **Education**: Educational institutions can benefit from Cisco SNA by safeguarding student and faculty data. The tool also helps in maintaining the integrity of online learning platforms and preventing disruptions.

 

Interactive Security Lab / Series 03

Microsegmentation & Lateral Movement Explorer

What happens after an attacker gains access to one workload? Change the segmentation policy, source zone, destination sensitivity and monitoring controls to see whether lateral movement is blocked, restricted or exposed.

01 / IDENTIFY Source workload
02 / SEGMENT Security boundary
03 / INSPECT Traffic and risk
04 / CONTAIN Enforcement action

Lateral Movement Simulator

Model an east-west connection attempt between workloads and inspect how segmentation policy changes the resulting security boundary.

Expected traffic High-risk behaviour

Segmentation Decision

The simulated enforcement engine explains whether the connection can cross the security boundary and what happens next.

CURRENT DECISION BLOCKED
Segmentation strength 92
Observed movement risk 20
SOURCE User workstation
→
POLICY Security boundary
→
DESTINATION Application server

Investigation signals

Enforcement response

Zero Trust Network Design

**Issue 1 – We Connect First and Then Authenticate**

  • Connect first, authenticate second.

TCP/IP is a fundamentally open network protocol facilitating easy connectivity and reliable communications between distributed computing nodes. It has served us well in enabling our hyper-connected world but—for various reasons—doesn’t include security as part of its core capabilities.

  • TCP has a weak security foundation

Transmission Control Protocol (TCP) has been around for decades and has a weak security foundation. When it was created, security was out of scope. TCP can detect and retransmit error packets but leave them to their default; communication packets are not encrypted, which poses security risks.

In addition, TCP operates with a Connect First, Authenticate, Second operation model, which is inherently insecure. It leaves the two connecting parties wide open for an attack. When clients want to communicate and access an application, they first set up a connection.

The authentication stage occurs only once the connect stage has been completed. Once the authentication stage has been completed, we can pass the data. 

zero trust network design
Diagram: Zero Trust security. The TCP model of connectivity.

From a security perspective, the most important thing to understand is that this connection occurs purely at a network layer with no identity, authentication, or authorization. The beauty of this model is that it enables anyone with a browser to easily connect to any public web server without requiring any upfront registration or permission. This is a perfect approach for a public web server but a lousy approach for a private application.

Zero Trust Connectivity: Service Networking APIs

**Understanding Zero Trust: A Paradigm Shift in Security**

In the context of service networking APIs, zero trust ensures that only authorized users and devices can interact with the APIs, reducing the risk of unauthorized access and data breaches. Implementing zero trust can significantly enhance the security posture of an organization, safeguarding sensitive data and maintaining user trust.

**Integrating Google Cloud and Zero Trust for Enhanced API Security**

Combining Google Cloud’s robust platform with zero trust principles creates a powerful synergy for securing service networking APIs. Google Cloud’s identity and access management tools, such as Cloud Identity and Access Management (IAM), work seamlessly within a zero trust framework to enforce strict authentication and authorization policies. By leveraging these tools, organizations can create a secure environment where APIs are protected from potential threats, and data is kept confidential and integral.

Service Networking API

**The potential for malicious activity**

With this process of Connect First and Authenticate Second, we are essentially opening up the door of the network and the application without knowing who is on the other side. Unfortunately, with this model, we have no idea who the client is until they have carried out the connect phase, and once they have connected, they are already in the network. Maybe the requesting client is not trustworthy and has bad intentions. If so, once they connect, they can carry out malicious activity and potentially perform data exfiltration. 

What is Network Monitoring?

Network monitoring is observing and analyzing network components and traffic to identify anomalies or performance issues. It uses specialized software and tools that provide real-time insights into network health, bandwidth utilization, device status, etc. By actively monitoring the network infrastructure, businesses can proactively detect and resolve issues before they escalate.

Network monitoring plays a pivotal role in safeguarding sensitive data from external threats. By monitoring network traffic for any suspicious activities or unauthorized access attempts, IT teams can quickly detect and respond to potential security breaches. Additionally, monitoring network devices for vulnerabilities and applying necessary patches and updates ensures a robust defense against cyber threats.

**Understanding Network Scanning**

Network scanning, at its core, involves systematically examining a network to identify its assets, configurations, and potential vulnerabilities. By employing various scanning techniques, security professionals can understand the network’s structure and potential risks.

Different methodologies for conducting network scanning exist, each catering to specific objectives. Passive scanning, for instance, focuses on observing network traffic without actively engaging with devices. On the other hand, active scanning involves sending requests to network devices to gather information about their configurations and potential vulnerabilities.

Numerous powerful tools are available to aid in network scanning endeavors. From widely used tools like Nmap and Wireshark to more specialized ones like Nessus and OpenVAS, the selection of tools depends on the desired scanning approach and the level of detail required. These tools provide many features, including port scanning, vulnerability assessment, and network mapping capabilities.

Additional Information on Network Mapping

Example: Identifying and Mapping Networks

To troubleshoot the network effectively, you can use a range of tools. Some are built into the operating system, while others must be downloaded and run. Depending on your experience, you may choose a top-down or a bottom-up approach.

**Developing a Zero Trust Architecture**

A zero-trust architecture requires endpoints to authenticate and be authorized before obtaining network access to protected servers. Then, real-time encrypted connections are created between requesting systems and application infrastructure. With a zero-trust architecture, we must establish trust between the client and the application before the client can set up the connection. Zero Trust is all about trust – never trust, always verify.

Trust is bidirectional between the client and the Zero Trust architecture (which can take forms ) and the application to the Zero Trust architecture. It’s not a one-time check; it’s a continuous mode of operation. Once sufficient trust has been established, we move into the next stage, authentication. Once authentication has been set, we can connect the user to the application. Zero Trust access events flip the entire security model and make it more robust. 

  • We have gone from connecting first and authenticating second to authenticating first and connecting second.
zero trust model
Diagram: The Zero Trust model of connectivity.

Example of a zero-trust network access

A. Single Pack Authorization ( SPA)

The user cannot see or know where the applications are located. SDP hides the application and creates a “dark” network by using Single Packet Authorization (SPA) for the authorization.

SPAs, also known as Single Packet Authentication, aim to overcome the open and insecure nature of TCP/IP, which follows a “connect then authenticate” model. SPA is a lightweight security protocol that validates a device or user’s identity before permitting network access to the SDP. The purpose of SPA is to allow a service to be darkened via a default-deny firewall.

The systems use a One-Time-Password (OTP) generated by algorithm 14 and embed the current password in the initial network packet sent from the client to the Server. The SDP specification mentions using the SPA packet after establishing a TCP connection. In contrast, the open-source implementation from the creators of SPA15 uses a UDP packet before the TCP connection.

B. Understanding Port Knocking

At its core, port knocking is an access control method that conceals open ports on a server. Instead of leaving ports visibly open and vulnerable to attackers, port knocking requires a sequence of connection attempts to predefined closed ports. Once the correct sequence is detected, the server dynamically opens the desired port and allows access. This covert approach adds an extra layer of protection, making it an intriguing choice for those seeking to fortify their network security.

Implementing port knocking within a zero-trust framework can significantly enhance your network security. By obscuring open ports and allowing access only to authorized users who possess the correct port-knocking sequence, potential attackers face an additional barrier to overcome. This technique effectively reduces the attack surface and minimizes the risk of unauthorized access, making it an invaluable tool for security-conscious individuals and organizations.

**Issue 2 – Fixed perimeter approach to networking and security**

Traditionally, security boundaries were placed at the edge of the enterprise network in a classic “castle wall and moat” approach. However, as technology evolved, remote workers and workloads became more common. As a result, security boundaries necessarily followed and expanded from just the corporate perimeter.

**The traditional world of static domains**

The traditional world of networking started with static domains. Networks were initially designed to create internal segments separated from the external world by a fixed perimeter. The classical network model divided clients and users into trusted and untrusted groups. The internal network was deemed trustworthy, whereas the external was considered hostile.

The perimeter approach to network and security has several zones. We have, for example, the Internet, DMZ, Trusted, and then Privileged. In addition, we have public and private address spaces that separate network access from here. Private addresses were deemed more secure than public ones as they were unreachable online. However, this trust assumption that all private addresses are safe is where our problems started. 

**The fixed perimeter** 

The digital threat landscape is concerning. We are getting hit by external threats to your applications and networks from all over the world. They also come internally within your network, and we have insider threats within a user group and internally as insider threats across user group boundaries. These types of threats need to be addressed one by one.

One issue with the fixed perimeter approach is that it assumes trusted internal and hostile external networks. However, we must assume that the internal network is as hostile as the external one.

Over 80% of threats are from internal malware or malicious employees. The fixed perimeter approach to networking and security is still the foundation for most network and security professionals, even though a lot has changed since the design’s inception. 

Zero Trust & VPC Service Controls

### Role of VPC Service Controls in Zero Trust Network Design

Zero Trust Network Design is rapidly gaining traction as an essential cybersecurity framework. Unlike traditional security models that assume trust within the network, Zero Trust operates on the principle of ‘never trust, always verify.’ This paradigm shift emphasizes the need for more granular controls and continuous verification of user and device identities. VPC Service Controls align perfectly with this approach by restricting access to critical resources and ensuring that only authenticated and authorized entities can interact with the data. This integration fortifies the network’s defenses, minimizes potential attack vectors, and ensures data integrity.

### Implementing VPC Service Controls in Google Cloud

Implementing VPC Service Controls within Google Cloud is a strategic move for organizations aiming to enhance their security posture. The process involves setting up security perimeters around sensitive resources, such as Cloud Storage buckets, BigQuery datasets, and Cloud Bigtable instances. By defining these perimeters, organizations can enforce policies that restrict access based on specific criteria, such as IP addresses, service accounts, or even user-defined attributes. This granular control not only prevents unauthorized access but also ensures compliance with industry regulations and standards.

VPC Security Controls

We get hacked daily!

We are now at a stage where 45% of US companies have experienced a data breach. The 2022 Thales Data Threat Report found that almost half (45%) of US companies suffered a data breach in the past year. However, this could be higher due to the potential for undetected breaches.

We are getting hacked daily, and major networks with skilled staff are crashing. Unfortunately, the perimeter approach to networking has failed to provide adequate security in today’s digital world. It works to an extent by delaying an attack. However, a bad actor will eventually penetrate your guarded walls with enough patience and skill.

If a large gate and walls guard your house, you would feel safe and fully protected inside. However, the perimeter protecting your home may be as large and thick as possible. There is still a chance that someone can climb the walls, access your front door, and enter your property. If a bad actor cannot even see your house, they cannot take the next step and try to breach your security.

Example: Security Scan Lynis

Lynis is an open-source security auditing tool designed to assess the security of Linux and Unix-based systems. Developed by CISOfy, Lynis performs comprehensive security scans by analyzing system configurations, checking for vulnerabilities, and recommending steps to improve overall security posture.

**Issue 3 – Dissolved perimeter caused by the changing environment**

The environment has changed with the introduction of the cloud, advanced BYOD, machine-to-machine connections, the rise in remote access, and phishing attacks. We have many internal devices and a variety of users, such as on-site contractors, that need to access network resources.

Corporate devices are also trending to move to the cloud, collocated facilities, and off-site to customer and partner locations. In addition, they are becoming more diversified with hybrid architectures.

These changes are causing major security problems with the fixed perimeter approach to networking and security. For example, with the cloud, the internal perimeter is stretched to the cloud, but traditional security mechanisms are still being used. But it is an entirely new paradigm. Also, some abundant remote workers work from various devices and places.

Again, traditional security mechanisms are still being used. As our environment evolves, security tools and architectures must evolve. Let’s face it: the network perimeter has dissolved as your remote users, things, services, applications, and data are everywhere. In addition, as the world moves to the cloud, mobile, and IoT, the ability to control and secure everything in the network is no longer available.

Phishing attacks are on the rise.

We have witnessed increased phishing attacks that can result in a bad actor landing on your local area network (LAN). Phishing is a type of social engineering where an attacker sends a fraudulent message designed to trick a person into revealing sensitive information to the attacker or to deploy malicious software on the victim’s infrastructure, like ransomware. The term “phishing” was first used in 1994 when a group of teens worked to obtain credit card numbers from unsuspecting users on AOL manually.

Phishing attacks
Diagram: Phishing attacks. Source is helpnetsecurity

Hackers are inventing new ways.

By 1995, they had created a program called AOHell to automate their work. Since then, hackers have continued to invent new ways to gather details from anyone connected to the internet. These actors have created several programs and types of malicious software that are still used today.

Recently, I was a victim of a phishing email. Clicking and downloading the file is very easy if you are not educated about phishing attacks. In my case, the particular file was a .wav file. It looked safe, but it was not.

**Issue 4 – Broad-level access**

So, you may have heard of broad-level access and lateral movements. Remember, with traditional network and security mechanisms, when a bad actor lands on a particular segment, i.e., a VLAN, known as zone-based networking, they can see everything on that segment. So, this gives them broad-level access. But, generally speaking, when you are on a VLAN, you can see everything in that VLAN and VLAN-to-VLAN communication is not the hardest thing to do, resulting in lateral movements.

The issue of lateral movements

Lateral movement is the technique attackers use to progress through the organizational network after gaining initial access. Adversaries use lateral movement to identify target assets and sensitive data for their attack. Lateral movement is the tenth step in the MITRE Att&ck framework. It is the set of techniques attackers use to move in the network while gaining access to credentials without being detected.

No intra-VLAN filtering

This is made possible as, traditionally, a security device does not filter this low down on the network, i.e., inside of the VLAN, known as intra-VLAN filtering. A phishing email can easily lead the bad actor to the LAN with broad-level access and the capability to move laterally throughout the network. 

For example, a bad actor can initially access an unpatched central file-sharing server; they move laterally between segments to the web developers’ machines and use a keylogger to get the credentials to access critical information on the all-important database servers.

They can then carry out data exfiltration with DNS or even a social media account like Twitter. However, firewalls generally do not check DNS as a file transfer mechanism, so data exfiltration using DNS will often go unnoticed. 

With a zero-trust network segmentation approach, networks are segmented into smaller islands with specific workloads. In addition, each segment has its own ingress and egress controls to minimize the “blast radius” of unauthorized access to data.

Example: Segmentation with Network Endpoint Groups (NEGs)

network endpoint groups

**Issue 5 – The challenges with traditional firewalls**

The limited world of 5-tuple

Traditional firewalls typically control access to network resources based on source IP addresses. This creates the fundamental challenge of securing admission. Namely, we need to solve the user access problem, but we only have the tools to control access based on IP addresses.

As a result, you have to group users, some of whom may work in different departments and roles, to access the same service and with the same IP addresses. The firewall rules are also static and don’t change dynamically based on levels of trust on a given device. They provide only network information.

Maybe the user moves to a more risky location, such as an Internet cafe, its local Firewall, or antivirus software that has been turned off by malware or even by accident. Unfortunately, a traditional firewall cannot detect this and live in the little world of the 5-tuple.  Traditional firewalls can only express static rule sets and not communicate or enforce rules based on identity information.

TCP 5 Tuple
Diagram: TCP 5 Tuple. Source is packet-foo.

**Issue 6 – A Cloud-focused environment**

Upon examining the cloud, let’s compare a public parking space. A public cloud is where you can put your car compared to your vehicle in your parking garage. We have multiple tenants who can take your area in a public parking space, but we don’t know what they can do to your car.

Today, we are very cloud-focused, but when moving applications to the cloud, we need to be very security-focused. However, the cloud environment is less mature in providing the traditional security control we use in our legacy environment. 

So, when putting applications in the cloud, you shouldn’t leave security to its default. Why? Firstly, we operate in a shared model where the tenant after you can steal your encryption keys or data. There have been many cloud breaches. We have firewalls with static rulesets, authentication, and key management issues in cloud protection.

**Control point change**

One of the biggest problems is that the perimeter has moved when you move to a cloud-based application. Servers are no longer under your control. Mobile and tablets exacerbate the problem as they can be located everywhere. So, trying to control the perimeter is very difficult. More importantly, firewalls only have access to and control network information and should have more content.

This perimeter is defined by ZTNA architecture and software-defined perimeter. Cloud users now manage firewalls by moving their applications to the cloud, not the I.T. teams within the cloud providers.

So when moving applications to the cloud, even though cloud providers provide security tools, the cloud consumer has to integrate security to have more visibility than they have today.

Before, we had clear network demarcation points set by a central physical firewall creating inside and outside trust zones. Anything outside was considered hostile, and anything on the inside was deemed trusted.

1. Connection-centric model

The Zero Trust model flips this around and considers everything untrusted. To do this, there are no longer pre-defined fixed network demarcation points. Instead, the network perimeter initially set in stone is now fluid and software-based.

Zero Trust is connection-centric, not network-centric. Each user on a specific device connected to the network gets an individualized connection to a particular service hidden by the perimeter.

Instead of having one perimeter every user uses, SDP creates many small perimeters purposely built for users and applications. These are known as micro perimeters. Clients are cryptographically signed into these microperimeters.

2. Micro perimeters: Zero trust network segmentation

The micro perimeter is based on user and device context and can dynamically adjust to environmental changes. So, as a user moves to different locations or devices, the Zero Trust architecture can detect this and set the appropriate security controls based on the new context.

The data center is no longer the center of the universe. Instead, the user on specific devices, along with their service requests, is the new center of the universe.

Zero Trust does this by decoupling the user and device from the network. The data plane is separated from the network to remove the user from the control plane, where the authentication happens first.

Then, the data plane, the client-to-application connection, transfers the data. Therefore, the users don’t need to be on the network to gain application access. As a result, they have the least privilege and no broad-level access.

3. Zero trust network segmentation

Zero-trust network segmentation is gaining traction in cybersecurity because it increases an organization’s network protection. This method of securing networks is based on the concept of “never trust, always verify,” meaning that all traffic must be authenticated and authorized before it can access the network.

This is accomplished by segmenting the network into multiple isolated zones accessible only through specific access points, which are carefully monitored and controlled.

Network segmentation is a critical component of a zero-trust network design. By dividing the network into smaller, isolated units, it is easier to monitor and control access to the network. Additionally, segmentation makes it harder for attackers to move laterally across the network, reducing the chance of a successful attack.

Zero-trust network design segmentation is essential to any organization’s cybersecurity strategy. By utilizing segmentation, authentication, and monitoring systems, organizations can ensure their networks are secure and their data is protected.

4. The I.P. address conundrum

Everything today relies on IP addresses for trust, but there is a problem: IP addresses lack user knowledge to assign and validate the device’s trust. There is no way for an IP address to do this. IP addresses provide connectivity but do not involve validating the trust of the endpoint or the user.

Also, I.P. addresses should not be used as an anchor for network locations as they are today because when a user moves from one place to another, the I.P. address changes. 

Can’t have security related to an I.P. address.

But what about the security policy assigned to the old IP addresses? What happens with your changed IPs? Anything tied to IP is ridiculous, as we don’t have a good hook to hang things on for security policy enforcement. There are several facets to policy. For example, the user access policy touches on authorization, the network access policy touches on what to connect to, and the user account policies touch on authentication.

With either one, there is no policy visibility with I.P. addresses. This is also a significant problem for traditional firewalling, which displays static configurations; for example, a stationary design may state that this particular source can reach this destination using this port number. 

**Security-related issues to I.P.**

  1. This has no meaning. There is no indication of why that rule exists or under what conditions a packet should be allowed to travel from one source to another.
  2. No contextual information is taken into consideration. When creating a robust security posture, we must consider more than ports and IP addresses.

For a robust security posture, you need complete visibility into the network to see who, what, when, and how they connect with the device. Unfortunately, today’s Firewall is static and only contains information about the network.

On the other hand, Zero Trust enables a dynamic firewall with the user and device context to open a firewall for a single secure connection. The Firewall remains closed at all other times, creating a ‘black cloud’ stance regardless of whether the connections are made to the cloud or on-premise. 

The rise of the next-generation firewall?

Next-generation firewalls are more advanced than traditional firewalls. They use the information in layers 5 through 7 (session, presentation, and application layers) to perform additional functions. They can provide advanced features such as intrusion detection, prevention, and virtual private networks.

Today, most enterprise firewalls are “next generation” and typically include IDS/IPS, traffic analysis and malware detection for threat detection, URL filtering, and some degree of application awareness/control.

Like the NAC market segment, vendors in this area began a journey to identity-centric security around the same time Zero Trust ideas began percolating through the industry. Today, many NGFW vendors offer Zero Trust capabilities, but many operate with the perimeter security model.

Still, IP-based security systems

NGFWs are still IP-based systems offering limited identity and application-centric capabilities. In addition, they are static firewalls. Most do not employ zero-trust segmentation, and they often mandate traditional perimeter-centric network architectures with site-to-site connections and don’t offer flexible network segmentation capabilities. Similar to conventional firewalls, their access policy models are typically coarse-grained, providing users with broader network access than what is strictly necessary.

Example: Tags and Controls with firewalling

Firewall tags

Interactive Security Lab / Series 03

Continuous Verification & Adaptive Access Lab

Trust is not permanent. Change the conditions of an active session and observe how continuous verification can trigger step-up authentication, restricted access or session revocation.

01 / ESTABLISH Initial trust
02 / MONITOR New telemetry
03 / REASSESS Dynamic risk
04 / ADAPT Enforcement

Active Session Simulator

Start with an established session, then introduce changes to identity, device posture, location and behaviour. The policy engine continuously reassesses whether the existing access level remains appropriate.

Normal behaviour Highly anomalous

Adaptive Policy Decision

The simulated policy compares the current session against its original trust assumptions and determines whether access should continue.

CURRENT SESSION STATE MAINTAIN
Current trust score 92
Current risk score 15
IDENTITY Verified
→
SESSION Active
→
RESOURCE Standard application

Continuous verification signals

Adaptive enforcement response

Ansible Variables

Ansible Variables | Ansible Automation

Ansible variables are one of the foundations of effective automation. They allow playbooks to separate automation logic from configuration data, making the same workflow reusable across different hosts, environments, applications, and deployment stages.

Instead of hard-coding values directly into tasks, variables can represent everything from IP addresses, ports, package versions, and service names to environment-specific configuration and credentials. This makes automation easier to maintain and allows a single playbook to adapt dynamically to the systems it manages.

Variables can be defined and consumed at several levels within Ansible, including inventory, playbooks, roles, host and group variables, and external variable files. Understanding variable scope and precedence is therefore essential. When the same variable is defined in multiple locations, Ansible follows a defined precedence model to determine which value ultimately takes effect.

Variables also become particularly powerful when combined with Jinja2 templating. Values can be inserted into configuration files, task parameters, conditions, loops, and templates, allowing playbooks to generate system configurations dynamically rather than relying on static files.

Not all variable values have to be defined in advance. Ansible can work with discovered system facts, registered task results, and dynamically evaluated expressions, allowing later tasks to react to information gathered during execution. This creates automation workflows that can make decisions based on the current state of the target infrastructure.

In larger environments, controlling sensitive variables becomes equally important. Ansible Vault provides encryption for secrets such as passwords, API keys, certificates, and other confidential configuration data, allowing sensitive values to remain protected while still being integrated into automated workflows.

The real value of Ansible variables is therefore not simply storing values. They provide the mechanism that connects inventory data, automation logic, system state, templates, and secure configuration into a reusable workflow. Understanding how variables are defined, resolved, overridden, templated, and protected is essential for building reliable Ansible automation at scale.

Ansible Variable

Interactive Ansible Lab / Variables 01

Ansible Variable Scope & Precedence

Variables separate automation logic from environment-specific data. Select a variable source and see how the simulated playbook resolves the final value when multiple definitions exist.

01 / DEFINE Variable
02 / STORE Variable source
03 / SCOPE Host / Play
04 / OVERRIDE Runtime
05 / RESOLVE Final value

Variable Resolver

Choose a variable and change the active source. The simulator highlights the value that wins in the current scenario.

ansible_host HOST VAR
10.10.10.11
Defined by: host_vars/router1.yml

Why this matters

A reusable playbook should avoid hard-coding values that change between systems or environments. Variables allow the same automation logic to operate against different hosts and groups.

Resolution Result

The simulated resolver compares several definitions of the same variable and identifies the value currently being used.

CURRENTLY RESOLVED VALUE
ansible_host 10.10.10.11

The host-specific variable is currently supplying the value used by the simulated playbook.

Variable layers

Inventory / host_vars 10.10.10.11
Group variables 10.10.10.10
Playbook vars 10.10.10.20
Extra vars (-e) 10.10.10.99

Engineering takeaway

Variables can be defined in multiple places. When values overlap, understanding precedence is essential for predictable automation.

Highlights: Ansible Variable

Managing & Configuring Systems

In the world of automation, Ansible has emerged as a popular choice for managing and configuring systems. One of the key features that sets Ansible apart is its ability to work with variables. Variables in Ansible enable users to define and store values that can be used throughout the playbook, making it a powerful tool for automation.

Variables in Ansible can be defined in various ways. They can be set globally, at the playbook level, or even at the task level. This flexibility allows users to customize their automation process based on their needs.

Variables: Values Specific to Different Environments

One everyday use case for variables is storing configuration values specific to different environments. For example, you have a playbook that deploys a web application. Using variables, you can define the database connection string, the server IP address, and other environment-specific values separately for development, staging, and production environments. This makes reusing the same playbook easy across different environments without modifying the code.

Variables: Dynamically Created

Another powerful feature of variables in Ansible is their ability to be dynamically generated. You can use calculated or fetched variables instead of hardcoding values at runtime. For example, the “lookup” plugin can read values from external sources like files or databases and assign them to variables. This makes your automation process more dynamic and adaptable.

Ansible Automation with Ansible Variables — Dynamic, Scalable, and Reusable Playbooks

Ansible variables are the backbone of flexible automation. They allow you to write one playbook that adapts to many environments, many hosts, and many configurations without rewriting tasks. Variables make automation dynamic, reusable, and scalable — essential for modern network engineering, DevOps, and infrastructure‑as‑code workflows.

Why Variables Matter — The Core of Dynamic Automation

Without variables, Ansible playbooks become static and repetitive. Variables allow you to:

  • customize configurations per host or group
  • store reusable values (IP addresses, VLANs, usernames, paths)
  • separate data from logic
  • build templates that adapt automatically
  • avoid hard‑coding values
  • support multi‑environment deployments (dev, test, prod)

Variables turn Ansible into a true automation engine rather than a simple scripting tool.

Where Variables Live — Multiple Scopes for Maximum Flexibility

Ansible supports variables at different levels, each with a specific purpose:

  • Inventory variables — host_vars and group_vars
  • Playbook variables — defined inside the play
  • Role variables — defaults and vars inside roles
  • Extra vars — passed at runtime for overrides
  • Facts — gathered automatically from hosts
  • Registered variables — created dynamically during tasks

This multi‑layer design allows you to build complex automation pipelines with clean separation of data and logic.

Host and Group Variables — Per‑Device Customization

Network automation relies heavily on host‑specific and group‑specific variables.

Examples:

host_vars/router1.yml
host_vars/router2.yml
group_vars/branch.yml
group_vars/datacenter.yml

These files store values such as:

  • interface names
  • IP addresses
  • BGP ASNs
  • VLAN lists
  • device credentials
  • site‑specific parameters

Playbooks remain generic while variables define the unique configuration for each device.

Registered Variables — Capturing Output for Logic Decisions

Registered variables store the output of a task so you can use it later in the playbook.

Example:

– name: Check interface status
ios_command:
commands: show ip interface brief
register: iface_status

You can then use iface_status.stdout to make decisions, validate state, or trigger conditional tasks.

Conditionals and Loops — Variables Drive Automation Logic

Variables allow Ansible to make decisions dynamically:

Conditionals -when: bgp_enabled == true

Loops -loop: “{{ vlans }}”

This makes automation adaptive — tasks run only when needed, and configurations scale automatically.

Templates — Jinja2 + Variables = Powerful Config Generation

Templates are where variables truly shine.

A Jinja2 template can generate full device configurations based on variables:router bgp {{ bgp_as }}
neighbor {{ peer_ip }} remote-as {{ peer_as }}

This allows you to build:

  • router configs
  • firewall policies
  • SD‑WAN templates
  • cloud infrastructure definitions
  • microservices configs

All driven by variables stored in host/group files.

Runtime Overrides — Extra Vars for CI/CD Pipelines

CI/CD systems often pass variables at runtime:

ansible-playbook deploy.yml -e “version=2.1.4”

This allows pipelines to:

  • deploy new versions
  • switch environments
  • trigger conditional logic
  • override defaults

Extra vars have the highest precedence, making them ideal for automation workflows.

Outcome — Clean, Scalable, Professional Automation

Using Ansible variables correctly results in:

  • reusable playbooks
  • clean separation of logic and data
  • scalable multi‑environment automation
  • dynamic configuration generation
  • reduced duplication
  • predictable, maintainable code

Variables are the foundation of professional‑grade automation — especially in networking, cloud, and DevOps.

**The Process of Decoupling**

For your automation journey, you want to be as flexible as possible. For this reason, within the Ansible architecture, we have a process known as decoupling. Here, we are separating site-specific code from static code. Anything specific to a server or managed device, such as an IP address, can be replaced with Ansible variables. As a best practice, always aim to have flexible playbooks, and if you want to share with someone else, all you need to change is the variables.

A) Variable Locations

As you know, variables are defined in several places. Each place you define the variables, such as the inventory with the ansible inventory variable or play header, will have an order of precedence. The most common place is for the Ansible set variables task. When you have Ansible set variables in the task, Ansible also allows setting variables directly in a task using the set_fact module. We will look at Ansible set variables in a task in a moment.

So, for an Ansible architecture with more extensive playbooks, remember the best place to hold your variables and not keep your playbooks site-specific. With Ansible, you can execute tasks and playbooks on multiple systems with a single command.

B) Ansible Tower

With Ansible Tower, you can have very complex automation requirements with the push of a button. Every site will have variations, and Ansible uses variables to manage system differences. To represent the variations among those systems, you can create variables with standard YAML syntax, including lists and dictionaries. 

 

Interactive Ansible Lab / Variables 02

Ansible Variables → Dynamic Configuration

Variables become especially powerful when combined with Jinja2 templates. Change the variable values below and watch the same template generate a different network configuration.

01 / VARIABLES Define data
02 / JINJA2 Apply template
03 / OUTPUT Generate config

Variable Inputs

Change the values used by the template. The generated configuration updates immediately.

# Jinja2 template
hostname {{ hostname }}
router bgp {{ bgp_as }}
  neighbor {{ peer_ip }} remote-as {{ peer_as }}

Generated Configuration

The template stays the same. Only the variable data changes.

VARIABLE → TEMPLATE → CONFIGURATION
hostname R1
→
{{ hostname }} Template placeholder
bgp_as 65001
→
{{ bgp_as }} BGP process value
peer_ip 10.0.0.2
→
{{ peer_ip }} Neighbor address
peer_as 65002
→
{{ peer_as }} Remote ASN
Template rendered successfully from the current variables.

Ansible Variable

Defining Ansible Variables 

Ansible is open-source automation and orchestration software that can automate most of your operations with IT infrastructure components, including servers, storage, networks, and application platforms. It is one of the most popular automation tools in the IT world and has strong community support, with more than 5,000 contributors worldwide. With Ansible, we use variables. 

Ansible uses variables to manage differences between systems. With Ansible, you can execute tasks and playbooks on multiple systems with a single command. To represent the variations among those systems, you can create variables with standard YAML syntax, including lists and dictionaries.

Ansible set variables in the task

Ansible is not a full-fledged programming language. However, it does have several programming language components. One of the most significant of these is variable substitution. The most straightforward way to define variables is to put a vars section in your playbook with the names and values of variables.

Here, we can have Ansible set variables in the task. The following will list the various ways you can set variables in Ansible. Keep in mind that we have an order of preference when doing so.

You can define your variables in several places: your playbooks, inventory, reusable files or roles, or at the command line. During a playbook run, you can create variables by registering a task’s return value or value as a new variable.

When defining variables in multiple places, they have variable precedence. After creating variables, you can use them in module arguments, such as conditional “when” clauses, templates, and loops. All of these are potent constructs to have in your automation toolbox.

Define variables: Vars: Section

If you are starting your automation journey, the simplest way to define variables is to put a vars section in your playbook with the names and values of variables. This allows you to define several configuration-related variables. So, to define variables in plays, include a vars: section in the header of the play where the variables are needed.

Variables defined in plays are only valid within that specific play and don’t have playbook scope. So, if you need a variable in a different play, you must define it again. This may be inconvenient and difficult to manage across extensive playbooks with multiple teams working on playbook development.

Define variables: Set_fact Module.

We also have the set_fact module. This module is used anywhere in a play to set variables. Any variable set this way applies as a fact to the host in which it is set. The set_fact relates the variable to the host used in the play.

Here, you can dynamically set variables based on the result of any task in the playbook. So, set_fact dynamically defines variables. Keep in mind that setting variables this way will have a playbook scope.

Define variables: Vars_files

You can also put variables into one or more files using the section called vars_files. This lets you put variables in a file instead of directly in the playbook. What I like most about setting variables this way is that it allows you to separate variables that contain sensitive information. When you define variables in reusable variable files, the sensitive variables are separated from playbooks.

This separation enables you to store your playbooks in, for example, source control software and even share them without the risk of exposing passwords or other sensitive personal data. So, when you put variables in files, they are referenced in the playbook using vars_files.

Use var_files to specify a list of files that include variables you want to include. This is convenient when you want to manage the variables independently of the place using them and is helpful for security purposes.

Define variables: Vars_Promt

So, here, we can use vars_promt in the play header to prompt users for a variable value. This has a playbook scope. By default, the variable is flagged as private, so the user does not see anything while entering it. We can if we have set private to no here. 

Define variables: Defining variables at runtime  

When you run your playbook, you can define variables by passing them at the command line using the—- extra-vars (or—e) argument. 

Define variables: Task Variables

Task variables are made from data discovered while executing tasks or in the fact-gathering phase of a play. These variables are host-specific and are added to the host’s host vars. Variables of this type can be discovered via gather_facts and fact modules, populated from task return data via the register task key, or defined directly by a task using the set_fact or add_host modules. 

**Facts / Variables**

Ansible Fact: System Variables

Ansible facts are a type of variable. You don’t define Ansible facts; they are discovered. Facts are system variables that contain information about the managed system. Each playbook starts with an implicit task to gather facts. This can also be disabled in the playhead. Gathering facts takes time, so if you are not going to use it, we can disable it.

We can also gather facts manually by using the setup modules. All of the facts are stored in one big variable called ansible_facts. However, within the variable, we have the second-tier variables. All of the facts are categorized into different variables. These facts are variables, and you can use the facts in conditionals and when statements. 

Speeding up Fact Gathering

Fact collection can be slow as you may work against many hosts you can use and set up a fact cache. If fact-caching is enabled, Ansible will store facts in a cache for the first time; it connects to the host. This is added to your Ansible.cfg with the “fact_caching_timeout” value. So if your playbook does not reference any ansible facts, you can turn off fact-gathering for that play. Here we use the “gather_facts” clause in the play and the Ansible.cfg.

**Ansible Inventory Variable**

Ansible Variables are a key component of Automation. They allow for dynamic play content and reusable plays across different sets of inventory. Variable data, such as specific details on how to connect to a particular host in your inventory, can be included along with an inventory in various ways.

While Ansible can discover data about a system during the setup phase, not all data can be found. We can define data with the inventory that will expand what Ansible has been to discover.

Ansible variables: The Ansible inventory variables

    • Host and group variables

In inventory, you can store variable values related to a specific host or group. This allows you to add variables directly to the hosts and groups in your main inventory file. Adding more managed nodes to your Ansible inventory will likely allow you to store variables in separate host and group variable files.

    • [host_var and group_var]

Ansible looks for host variable files in a directory called host_vars and group variable files in a directory called group_vars. Remember that Ansible expects these directories to be in either the directory that contains your playbooks or the directory adjacent to your inventory file. You can break things out even further if you want to go further. Ansible lets us define, for example, group_vars/production as a directory instead of a file. 

Behavior inventory variables

Behavioral inventory parameters allow you to describe your machines with additional parameters in your inventory file. Such as the ansible_connection parameter may be helpful. By default, Ansible supports multiple means of transport, which Ansible uses to connect to the managed host. Here is a list of some expected behavior inventory variables and the behaviors they intend to modify:

    1. ansible_host: This is the DNS name or the Docker container name that Ansible will initiate a connection to.
    2. ansible_port: This specifies the port number that Ansible will use to connect to the inventory host if it is not the default value 22.
    3. ansible_user: This specifies the username Ansible will use to communicate with the inventory host, regardless of the connection type.

**A key point: Ansible inventory scalability**

If you don’t have many hosts, you can use the inventory to store host and group variables. However, as your environment grows, managing variables in the inventory will become more challenging. In this case, you need to find a more scalable approach to keeping track of your host and group variables.

Even though Ansible variables can hold Booleans, strings, lists, and dictionaries, you can specify only Booleans and strings in an inventory file. Therefore, we have a more scalable approach to keeping track of host and group variables: you can create a separate variable file for each host and group. Ansible expects these variable files to be in YAML format, which allows you to break the inventory into multiple files.

Highlighting Ansible conditionals

– In a playbook, you may want to execute different tasks depending on the value of a fact, a variable, or the result of a previous task. You may wish to the value of some variables to depend on the value of other variables. You can do all of these things with conditionals. Ansible uses Jinja2 tests and filters in conditionals. Basic conditionals are used with the when clause.

– The most straightforward conditional statement applies to a single task. Create the Task, then add a when statement that applies a test. When running the task or playbook, Ansible evaluates the test for all hosts. For example, if you are installing MySQL on multiple machines, some of which have SELinux enabled, you might have a task to configure SELinux to allow MySQL to run.

– You would only want that Task to run on machines with SELinux enabled. Sometimes, you want to execute or skip a task based on facts. With conditionals based on facts, you can install a specific package only when the operating system is a particular version. You can skip configuring a firewall on hosts with internal IP addresses. You can perform cleanup tasks only when a filesystem is getting full.

**Ansible conditionals and When Clause**

You can also create conditionals based on variables defined in the playbooks or inventory. So, we have playbook variables, registered variables, and facts that can all be used in conditions and ensure that tasks only run if specific conditions are true. 

**Handlers and When Statement**

There are several ways Ansible can be configured for conditional task execution. We have, for example, Handlers for conditional task execution. It is used when a task has changed something. Then we have a very powerful When statement. The When statement allows you to run tasks when specific conditions are true. You can also use the Register in combination with When statements.

**Using Handlers for conditional task execution**

A handler is a task only executed when triggered by a task that has changed something. Handler is executed after all tasks in a play, so you need to organize the contents of your playbook accordingly. If any task fails after the task is called the handler, the handlers are not executed. We can use the “force-handler: to change this.

Keep in mind that handlers are operational when something has changed. We have a force handler that allows you to force the handler to be started even if subsequent tasks are failing. Simply put, a handler is a particular type of Task that is called only if something changes. 

**Handlers in Pre and Post tasks**

Each task section in a playbook is handled separately; any handler notified in pre_tasks, tasks, or post_tasks is executed at the end of each section. As a result, it is possible to execute one handler several times in one play.

  • Using Blocks

Blocks create logical groups of tasks. Blocks also offer ways to handle task errors, similar to exception handling in many programming languages. Blocks can also be used in error conditional handling. So you have to use a block to define the main Task to run and then rescue to define tasks that run if tasks defined in the block fail. You can use “Always to Define” tasks that will run regardless of the success or failure of the Block and Rescue.

  • Ansible loops

What happens, however, if you have a single task but need to run it against a list of data, for example, creating several user accounts, directories, or something more complex? Like any programming language, Ansible loops provide an easier way of executing repetitive tasks using fewer lines of code in a playbook.

Examples of commonly used loops include changing ownership of several files and directories with the file module, creating multiple users with the user module, and repeating a polling step until a particular result is reached.

**A final point: Managing failures with the Fail Module**

Ansible looks at a task’s exit status to determine whether it has failed. When any task fails, Ansible aborts the rest of the playbook on that host and continues with the next host. We can change this with a few solutions. For example, we can use ignore_errors in a task to ignore failure—force_handlers to force a handler that has been triggered to run even if another task fails.

But remember, there needs to be a change. We can also use these Failed_When, which allows you to specify what to look for in command output to recognize a failure. You may have a playbook used to clean up resources, and you want the playbook to ignore every error, keep going on till the end, and then fail if there are errors. In this case, when using the fail module, the failing Task must have Ignore Errors set to yes.

Interactive Ansible Lab / Variables 03

Ansible Conditional Automation Lab

Variables do more than provide configuration values. They can control whether tasks run, drive loops, and expose information returned by earlier tasks through registered variables.

01 / VARIABLES Define state
02 / WHEN Evaluate condition
03 / LOOP Repeat work
04 / RESULT Continue automation

Automation Inputs

Change the variables below. The task engine will show which simulated tasks execute and which are skipped.

BGP enabled true
Interface state up
Deployment mode production
VLAN set 3 items
# Conditional task
when: bgp_enabled == true

# Loop
loop: "{{ vlans }}"

# Registered result
register: iface_status

Task Execution

This simulation evaluates the variables and shows the resulting automation path.

Configure BGP
RUN
when: bgp_enabled == true
Condition evaluated to true.
Validate interface
RUN
registered: iface_status
The registered interface result is available.
Deploy production settings
RUN
when: environment == "production"
The environment matches production.
Configure VLANs
RUN
loop: "{{ vlans }}"
The task will iterate through the selected VLAN list.
AUTOMATION SUMMARY
4 task paths evaluated. BGP and production tasks are active.
Ansible Architecture Diagram

Ansible Architecture | Ansible Automation

Ansible Architecture

Ansible uses a deliberately simple architecture: automation is defined centrally and executed against managed infrastructure without requiring a traditional agent on each target system. This agentless control model is one of the reasons Ansible is widely used for server configuration, application deployment, network automation, and infrastructure operations.

At the centre of the architecture is the Ansible control node. This is where the Ansible engine, inventories, playbooks, roles, variables, and supporting configuration are maintained. The control node determines what should happen, selects the target systems, resolves variables, and coordinates execution across the environment.

The systems being automated are known as managed nodes. These can include Linux or Windows servers, virtual machines, cloud instances, containers, and network devices. Depending on the platform, Ansible can communicate using mechanisms such as SSH, WinRM, or network-specific connection methods. The managed node does not normally require a continuously running Ansible agent.

Inventory provides Ansible with its view of the infrastructure. It defines which hosts are available and allows them to be organised into logical groups. Static inventories can be maintained as files, while dynamic inventory sources can obtain infrastructure information from cloud platforms, APIs, or other systems.

Playbooks describe the desired automation workflow using YAML. They contain plays and tasks that determine which hosts should be targeted and what operations should be performed. Variables, conditionals, loops, handlers, and roles can then be used to turn a simple task list into a reusable automation workflow.

When a task is executed, Ansible typically uses an Ansible module to perform the required operation on the managed node. Modules provide the functional interface for activities such as installing packages, managing files, configuring services, creating users, or changing network configuration. Ansible gathers the result of the operation and uses that information to determine the next stage of execution.

This creates a clear execution path: inventory identifies the targets, the playbook defines the desired workflow, variables provide the data, and modules perform the work. Understanding how these components interact is essential when troubleshooting automation, designing reusable roles, or scaling Ansible across larger environments.

Ansible’s architecture is therefore simple by design, but the underlying execution model is powerful. Once the relationship between the control node, managed nodes, inventory, playbooks, modules, roles, and connection methods is understood, Ansible becomes much easier to design, troubleshoot, and operate at scale.

Interactive Ansible Architecture / Core 01

Ansible Architecture

Ansible uses a controller-driven, agentless architecture. Automation logic lives on the control node while inventory identifies the targets, modules perform the work, and managed nodes receive the resulting configuration over SSH, WinRM, or APIs.

01 / CONTROL Automation brain
02 / INVENTORY Identify targets
03 / MODULES Execute work
04 / TARGETS Managed nodes

Core Architecture

Select a component to see its role in the Ansible automation architecture.

Ansible Control Node
CONTROL
Stores and executes the automation logic.
↓
Inventory
TARGETS
Defines hosts, groups, connection information and variables.
↓
Playbooks & Roles
LOGIC
Describe the desired automation workflow using YAML.
↓
Modules
ENGINE
Perform specific configuration or information-gathering tasks.
↓
SSH / WINRM / API
WEB-01 Linux server
ROUTER-01 Network device
FIREWALL-01 Security device

Architecture Detail

Explore what each component contributes to the automation pipeline.

CONTROL NODE
The Automation Brain
The control node is where Ansible is installed and executed. It orchestrates the automation process and communicates with managed nodes without requiring an Ansible agent on those systems.
✓ Stores playbooks and automation logic
✓ Loads inventory and variables
✓ Initiates remote execution
✓ No resident Ansible agent is required on targets
control_node: ansible-controller
contains:
  - playbooks
  - inventory
  - roles
  - variables
  - collections

Highlights: Ansible Architecture

Architecture: The Components

Ansible has emerged as one of the most popular automation tools, revolutionizing how organizations manage and deploy their IT infrastructure. With its simple yet robust architecture, Ansible has gained widespread adoption across diverse industries. In this blog post, we will delve into the intricacies of Ansible architecture, exploring how it works and its key components.

At its core, Ansible follows a client-server architecture model. It has three main components: control nodes, managed nodes, and communication channels. Let’s examine each of these components.

1. Control Node:

The control node acts as the central management point in Ansible architecture. It is the machine from which Ansible is installed and executed. The control node stores the inventory, playbooks, and modules to manage the managed nodes. Ansible uses a declarative language called YAML to define playbook tasks and configurations.

2. Managed Nodes:

Managed nodes are the machines that Ansible drives. They can be physical servers, virtual machines, or even network devices. Ansible connects to managed nodes over SSH or WinRM protocols, enabling seamless management across different operating systems.

3. Communication Channels:

Ansible utilizes SSH or WinRM protocols to establish secure communication channels between the control node and managed nodes. SSH is used for Linux-based systems, whereas WinRM is for Windows-based systems. This allows Ansible to execute commands, transfer files, and collect information from managed nodes.

Ansible Architecture with Ansible Automation — How the Engine Actually Works

Ansible’s architecture is intentionally simple, but extremely powerful. It is built around a controller‑driven model, where automation logic lives centrally and executes remotely over SSH or API calls. This section explains how Ansible works internally, how automation flows through its components, and how variables, inventories, roles, and modules interact to create a full automation pipeline.

1. The Ansible Control Node — The Automation Brain

The control node is where automation begins. It stores:

  • playbooks
  • roles
  • templates
  • inventories
  • variable files
  • collections and modules

The control node does not require agents on remote devices. Instead, it uses:

  • SSH (for Linux/network devices)
  • WinRM (for Windows)
  • API calls (for cloud platforms)

This agentless architecture is one of Ansible’s biggest advantages — no daemons, no agents, no lifecycle management.

2. Managed Nodes — The Devices Being Automated

Managed nodes are the systems Ansible configures:

  • routers, switches, firewalls
  • Linux servers
  • Windows servers
  • cloud resources (AWS, Azure, GCP)
  • containers and Kubernetes objects

Ansible pushes configuration to these nodes using modules, templates, and variables.

Managed nodes remain lightweight — they only need Python or a network API.

3. Inventory — The Source of Truth for All Targets

The inventory defines what Ansible automates. It can be:

  • static inventory files
  • dynamic inventory scripts
  • cloud‑native inventory plugins
  • host_vars and group_vars directories

Inventory is the architectural foundation because it determines:

  • host groups
  • device roles
  • variables per host
  • environment separation (dev/test/prod)

Automation becomes scalable when inventory is structured cleanly.

3. Inventory — The Source of Truth for All Targets

The inventory defines what Ansible automates. It can be:

  • static inventory files
  • dynamic inventory scripts
  • cloud‑native inventory plugins
  • host_vars and group_vars directories

Inventory is the architectural foundation because it determines:

  • host groups
  • device roles
  • variables per host
  • environment separation (dev/test/prod)

Automation becomes scalable when inventory is structured cleanly.

4. Modules — The Execution Engine of Ansible

Modules are small programs that perform tasks on managed nodes.

Examples:

  • ios_config, nxos_command, junos_facts (network modules)
  • copy, template, file, service (system modules)
  • ec2, azure_rm, gcp_compute (cloud modules)

Modules are idempotent, meaning they only make changes when required — a core architectural principle of Ansible.

5. Playbooks — The Automation Logic Layer

Playbooks define what automation should do.

They orchestrate:

  • tasks
  • handlers
  • variables
  • roles
  • conditionals
  • loops
  • templates

Playbooks are declarative — you describe the desired state, and Ansible figures out how to achieve it.

6. Roles — Structured, Reusable Automation Components

Roles provide architectural structure for large automation projects.

A role contains:

  • tasks
  • templates
  • variables
  • handlers
  • files
  • defaults
  • metadata

Roles allow teams to build modular automation pipelines that scale across hundreds of devices.

7. Variables — The Dynamic Data Layer

Variables make automation flexible.

They can live in:

  • inventory
  • host_vars
  • group_vars
  • role defaults
  • role vars
  • playbooks
  • extra vars
  • registered variables

Variables allow one playbook to configure:

  • 100 routers
  • 50 firewalls
  • 20 Kubernetes clusters

…each with different parameters.

Templates — Dynamic Configuration Generation

Templates (Jinja2) generate device configs dynamically:

interface {{ iface }}
ip address {{ ip }} {{ mask }}

Templates allow automation to produce:

  • router configs
  • firewall policies
  • cloud manifests
  • microservices YAML
  • SD‑WAN templates

All driven by variables and inventory.

9. Execution Flow — How Ansible Runs Automation

The architectural workflow looks like this:

  1. User runs a playbook
  2. Ansible loads inventory
  3. Variables are merged from all scopes
  4. Tasks execute modules on managed nodes
  5. Modules return JSON results
  6. Registered variables capture output
  7. Conditionals decide next steps
  8. Templates generate configs
  9. Handlers run if changes occurred
  10. Results are displayed and logged

This flow is simple but extremely powerful.

10. Automation at Scale — Controller + Collections + CI/CD

Modern Ansible automation uses:

  • Automation Controller / AWX for GUI‑driven orchestration
  • Collections for vendor‑specific modules
  • GitOps pipelines for version control
  • CI/CD triggers for automated deployments
  • Dynamic inventories for cloud‑native environments

This transforms Ansible from a scripting tool into a full automation platform.

Outcome — A Clean, Modular, Scalable Automation Architecture

Ansible’s architecture delivers:

  • agentless automation
  • modular design
  • reusable roles
  • dynamic variables
  • scalable inventories
  • cloud + network + server automation
  • predictable, idempotent execution

It is one of the simplest architectures in automation — yet one of the most powerful.

**Ansible Workflow**

Ansible operates on a push-based model, where the control node pushes configurations and commands to the managed nodes. The workflow involves the following steps:

1. Inventory: The inventory is a file that contains a list of managed nodes. It provides Ansible with information such as IP addresses, hostnames, and connection details to establish communication.

2. Playbooks: Playbooks are YAML files that define the desired state of the managed nodes. They consist of a series of tasks or plays, each representing a specific action to be executed on the managed nodes. Playbooks can be as simple as a single task or as complex as a multi-step deployment process.

3. Execution: Ansible executes playbooks on the control node and communicates with managed nodes to perform the defined tasks. It uses modules, which are small programs written in Python or other scripting languages, to interact with the managed nodes and carry out the required operations.

4. Reporting: Ansible provides detailed reports on task execution status, allowing administrators to monitor and troubleshoot any issues. This helps maintain visibility and consistently apply the desired configurations across the infrastructure.

Advantages of Ansible Architecture:

The Ansible architecture offers several advantages, making it a preferred choice for automation:

1. Simplicity: Ansible’s architecture is designed to be simple and easy to understand. Using YAML playbooks and declarative language allows administrators to define configurations and tasks in a human-readable format.

2. Agentless: Unlike traditional configuration management tools, Ansible requires no agent software installed on managed nodes. This reduces complexity and eliminates the need for additional overhead.

3. Scalability: Ansible’s highly scalable architecture enables administrators to manage thousands of nodes simultaneously. SSH and WinRM protocols allow for efficient communication and coordination across large infrastructures.

**Playbooks and Inventory**

 As a best practice, you don’t want your Ansible architecture, which consists of playbooks and Inventory, to be too specific. However, you need to have a certain level of abstraction and keep out precise information. Therefore, to develop flexible code, you must separate site-specific information from the code, which is done with variables in Ansible. 

Remember that when you develop dynamic code along with your static information, you can use this on any site with minor modifications to the variables themselves. However, you can have variables in different places, and where you place variables, such as a play header or Inventory, will take different precedence. So, variables can be used in your Ansible deployment architecture to provide site-specific code. 

 

Interactive Ansible Architecture / Execution 02

How Ansible Executes Automation

Ansible turns a playbook into an execution pipeline. Inventory identifies the targets, variables provide context, tasks invoke modules, and the resulting state determines what happens next.

01 / TARGET Load inventory
02 / CONTEXT Resolve variables
03 / TASK Select module
04 / EXECUTE Change state
05 / RESULT Continue flow

Execution Pipeline

Select a stage above or use the controls below to follow a simulated Ansible run from inventory discovery to the final result.

01 Load Inventory ACTIVE
Ansible determines which hosts match the play's host pattern.
↓
02 Resolve Variables WAIT
Inventory, host, group, role and other variable sources provide the data required by the task.
↓
03 Run Task / Module WAIT
A task invokes the appropriate module with its arguments and desired state.
↓
04 Apply Desired State WAIT
The module checks the current state and changes the managed node only when required.
↓
05 Return Result WAIT
The result is returned to Ansible and can influence conditionals, registered variables, handlers and later tasks.
ANSIBLE ENGINE STATE
Target web-01
Module package
Desired state nginx=present
Result pending

Execution Detail

Each stage produces information that the next stage can consume.

STAGE 01 / TARGET
Inventory Determines the Target
Ansible first evaluates the play's host pattern against the inventory. This determines which managed nodes participate in the execution.
Host pattern webservers
Matched hosts web-01, web-02
Connection SSH
- name: Configure web servers
hosts: webservers
tasks:
  - install nginx
CURRENT EXECUTION STATE
Inventory has identified the systems that will receive the automation.

Ansible Architecture

Rise of Distributed Computing

Several megatrends have driven the move to an Ansible architecture. Firstly, the rise of distributed computing made the manual approach to almost anything in the IT environment obsolete. This was not only because it caused many errors and mistakes but also because the configuration drift from the desired to the actual state was considerable.

This is not only an operational burden but also a considerable security risk. Today, deploying applications by combining multiple services that run on a distributed set of resources is expected. As a result, configuration and maintenance are more complex than in the past.

Two Options:

– You have two options to implement all of this. First, you can connect these services by manually spinning up the servers, installing the necessary packages, and SSHing to each one, or you can go down the path of automation, in particular, automation with Ansible.

– So, with Ansible deployment architecture, we have the Automation Engine, the CLI, and Ansible Tower, which is more of an automation platform for enterprise-grade automation. This post focuses on Ansible Engine. 

– As a quick note, if you have environments with more than a few teams automating, I recommend Ansible Tower or the open-source version of AWX. Ansible Tower has a 60-day trial license, while AWX is fully open-sourced and does not require a license. The open-source version of AWX could be a valuable tool for your open networking journey.

A Key Point: Risky: The Manual Way.

Let me put it this way: If you configure manually, you will likely maintain all the settings. What about mitigating vulnerabilities and determining what patches or packages are installed in a large environment?

How can you ensure all your servers are patched and secured manually? Manipulating configuration files by hand is tedious, error-prone, and time-consuming. Equally, performing pattern matching to make changes to existing files is risky. 

A Key Point: The issue of Configuration Drift

The manual approach will result in configuration drift, where some servers will drift from the desired state. Configuration drift is caused by inconsistent configuration items across devices, usually due to manual changes and updates and not following the automation path. Ansible is all about maintaining the desired state and eliminating configuration drift.

Components: Ansible Deployment Architecture

Configuration management

The Ansible architecture is based on a configuration management tool that can help alleviate these challenges. Ansible replaces the need for an operator to tune configuration files manually and does an excellent job in application deployment and orchestrating multi-deployment scenarios. It can also be integrated into CI/CD pipelines.

In reality, Ansible is relatively easy to install and operate. However, it is not a single entity. Instead, it comprises tools, modules, and software-defined infrastructure that form the ansible toolset configured from a single host that can manage multiple hosts.

We will discuss the value of idempotency with Ansible modules later. Even with modules’ idempotency, you can still have users of Ansible automate over each other. Ansible Tower or AWS is the recommended solution for multi-team automation efforts.

Ansible vs Tower
Diagram: Ansible vs Tower. Source Red Hat.

Pre-deployed infrastructure: Terraform

Ansible does not deploy the infrastructure; you could use other solutions like Terraform that are best suited for this. Terraform is infrastructure as a code tool. Ansible Engine is more of a configuration as code. The physical or virtual infrastructure needs to be there for Ansible to automate, compared to Terraform, which does all of this for you.

Ansible is an easy-to-use DevOps tool that manages configuration as code has in the same design through any sized environment. Therefore, the size of the domain is irrelevant to Ansible.

As Ansible Connectivity uses SSH that runs over TCP, there are multiple optimizations you can use to increase performance and optimize connectivity, which we will discuss shortly. Ansible is often described as a configuration management tool and is typically mentioned along the same lines as Puppet, Chef, and Salt. However, there is a considerable difference in how they operate. Most notably, the installation of agents.

Ansible architecture: Agentless

The Ansible architecture is agentless and requires nothing to be installed on the managed systems. It is also serverless and agentless, so it has a minimal footprint. Some configuration management systems, such as Chef and Puppet, are “pull-based” by default.

Where agents are installed, periodically check in with the central service and pull-down configuration. Ansible is agentless and does not require the installation of an agent on the target to communicate with the target host.

However, it requires connectivity from the control host to the target inventory ( which contains a list of hosts that Ansible manages) with a trusted relationship. For convenience, we can have passwordless sudo connectivity between our hosts. This allows you to log in without a password and can be a security risk if someone gets to your machines; they could have escalated privileges on all the Ansible-managed hosts.

Agentless Automation
Diagram: Agentless Automation. Source Docs at Ansible

Key Ansible features:

Easy-to-Read Syntax: Ansible uses the YAML file format and Jinja2 templating. Jinja2 is the template engine for the Python programming language. Ansible uses Jinja2 templating to access variables and facts and extends the defaults of Ansible for more advanced use cases.

Not a full programming language: Although Ansible is not a full-fledged programming language, it has several good features. One of the most important is a variable substitution, or using the values of variables in strings or other variables.

In addition, the variables in Ansible make the Ansible playbooks, which are like executable documents, very flexible. Variables are a powerful construct within Ansible and can be used in various ways. Nearly every single thing done in Ansible can include a variable reference. We also have dynamic variables known as facts.

Jinja2 templating language: Ansible’s defaults are extended using the Jinja2 templating language. In addition, Ansible’s use of Jinja2 templating adds more advanced use cases. One great benefit is that it is self-documenting, so when someone looks at your playbook, it’s easy to understand, unlike Python code or a Bash script.

So, not only is Ansible easy to understand, but with just a few lines of YAML, the language used for Ansible, you can install, let’s say, web servers on as many hosts as you like.

Ansible Architecture: Scalability: Ansible can scale. For example, Ansible uses advanced features like SSH multiplexing to optimize SSH performance. Some use cases manage thousands of nodes with Ansible from a single machine.

SSH Connection: Parallel connections: We have three managed hosts: web1, web2, and web3. Ansible will make SSH connections parallel to web1, web2, and web3. It will then execute the first task on the list on all three hosts simultaneously. In this example, the first task is installing the Nginx package, so the task in the playbook would look like this. Ansible uses the SSH protocol to communicate with hosts except Windows hosts.

This SSH service is usually integrated with the operating system authentication stack, enabling you to use Kerberos to improve authentication security. Ansible uses the same authentication methods that you are already familiar with. SSH keys are typically the easiest way to proceed as they remove the need for users to input the authentication password every time a playbook is run.

Optimizing SSH 

Ansible uses SSH to manage hosts, and establishing an SSH connection takes time. However, you can optimize SSH with several features. Because the SSH protocol runs on top of the TCP protocol, you need to create a new TCP connection when you connect to a remote host with SSH.

You don’t want to open a new SSH connection for every activity. Here, you can use Control Master, which allows multiple simultaneous SSH connections with a remote host using one network connection. Control Persists, or multiplexing keeps a connection option for xx seconds. The pipeline allows more commands to use simultaneous SSH connections. If you recall how Ansible executes a task?

    1. It generates a Python script based on the module being invoked
    2. It then copies the Python script to the host
    3. Finally, it executes the Python script

The pipelining optimization will execute the Python scripts by piping it to the SSH sessions instead of copying git. Here, we are using one SSH session instead of two. These options can be configured in Ansible.cfg, and we use the SSH_connecton section. Then, you can specify how these connections are used.  

A note on scalability: Ansible and modularity

Ansible scales down well because simple tasks are easy to implement and understand in playbooks. Ansible scales well because it allows decomposing of complex jobs into smaller pieces. So, we can bring the concept of modularity into playbooks as the playbook becomes more difficult. I like using Tags for playbook developments that can save time and effect to test different parts of the playbook when you know certain parts are 100% working.

Security wins! No daemons and no listening agents

Once Ansible is installed, it will not add a database, and there will be no daemons to start or keep running. You only need to install it on one machine (which could easily be a laptop), and it can manage an entire fleet of remote machines from that central point. No Ansible agent is listening on a port. Therefore, when you use Ansible, there is no extra attack surface for a bad actor to play with.

This is a big win for security following one of the leading security principles of reducing the attack surface. When you run the Ansible-playbook command, Ansible connects to the remote servers and does what you want. By its defaults, Ansible is pretty streamlined out of the box, but you can enhance it by configuring Ansible.cfg file, to be even more automated.

Ansible Architecture: Ansible Architecture diagram

Ansible Inventory: Telling Ansible About Your Servers

The Ansible architecture diagram has several critical components. First, the Ansible inventory is all about telling Ansible about your servers. Ansible can manage only the servers it explicitly knows about. Ansible comes with one default server of the local host, the control host.

You provide Ansible with information about servers by specifying them in an inventory. We usually create a directory called “inventory” to hold this information.

Example: For example, a straightforward inventory file might contain a list of hostnames. The Ansible Inventory is the system against which a playbook runs. It is a list of systems in your infrastructure against which the automaton is executed. The following Ansible architecture diagram shows all the Ansible components, including modules, playbooks, and plugins.

Ansible Architecture Diagram
Diagram: Ansible Architecture Diagram.

Ansible architecture diagram: Inventory highlights

These hosts commonly have hosts but can comprise other components such as network devices, storage arrays, and other physical and virtual appliances. It also has valuable information that can be used alongside our target using the execution.

The Inventory can be as simple as a text file or more dynamic, where it is an executable where the data is sourced dynamically. This way, we can store data externally and use it during runtime. So, we can have a dynamic inventory via Amazon Web Service or create our own dynamic.

Example: AWS EC2 External inventory script  

The above Ansible architecture diagram shows a connection to the cloud. If you use Amazon Web Services EC2, maintaining an inventory file might not be the best approach because hosts may come and go over time, be managed by external applications, or be affected by AWS autoscaling.

For this reason, you can use the EC2 external inventory script. In addition, if your hosts run on Amazon EC2, then EC2 tracks information about your hosts for you. Ansible inventory is flexible; you can use multiple inventory sources simultaneously. Mix dynamic and statically managed inventory sources in the same ansible run is possible. Many are referring to this as an instant hybrid cloud.

Ansible and NMAP
Diagram: Ansible and NMAP. Source Red Hat.

Ansible deployment architecture and Ansible modules

Next, within the Ansible deployment architecture, we have the Ansible modules, which are considered Ansible’s main workhorse. You use modules to perform various tasks, such as installing a package, restarting a service, or copying a configuration file. Ansible modules cater to a wide range of system administration tasks.

This list has categories of the kinds of modules that you can use. There are over 3000 modules. So you may be thinking, who is looking after these modules? That’s where collections in the more recent versions of Ansible are doing it.

**Extending Ansible Modules**

The modules are the scripts (written in Python) that come with packages with Ansible and Perform some action on the managed host. Ansible has extensive modules covering many areas, including networking, cloud computing, server configuration, containerization, and virtualization.

In addition, many modules support your automation requirements. If no modules exist, you can create a custom module with the extensive framework of Ansible. Each task is correlated with the module one-to-one. For example, a template task will use the template module.

**A key point: Idempotency**

Modules strive to be idempotent, allowing the module to run repeatedly without a negative impact. In Ansible, the input is in the form of command-line arguments to the module, and the output is delivered as JSON to STDOUT. Input is generally provided in the space-separated key=value syntax, and it’s up to the module to deconstruct these into usable data.

Most Ansible modules are also idempotent, which means running an Ansible playbook multiple times against a server is safe. For example, if the deploy user does not exist, Ansible will create it. If it does exist, Ansible will not do anything. This is a significant improvement over the shell script approach, where running the script a second time might have different and potentially unintended effects.

**The Use of Ansible Ad Hoc Commands**

For instance, if you wanted to power off all of your labs for the weekend, you could execute a quick one-liner in Ansible without writing a playbook. With an Ad Hoc command, we can be suitable for running one command on a host, and it uses the modules. They are used to check configuring on the host and are also good for learning Ansible.

Note that Ansible is the executable for ad hoc one-task executions, and ansible-playbook is the executable for processing playbooks to orchestrate multiple tasks.

The opposable side of the puzzle is the playbook commands are used for more complex tasks and are better for use cases where the dependencies have to be managed. The playbook can take care of all application deployments and dependencies.

Ansible Ad Hoc commands
Diagram: Ansible Commands. Source Docs at Ansible.

Ansible Plays

1.Ansible playbooks

An Ansible Playbook can have multiple plays that can exist within an Ansible playbook that can execute on different managed assets. So, an Ansible Play is all about “what am I automating.” Then, it connects to the hosts to perform the actions. Each playbook comprises one or more ‘plays’ in a list. The goal of a play is to map a group of hosts to some well-defined roles, represented by things Ansible calls tasks.

At a basic level, a task is just a call to an Ansible module. By composing a playbook of multiple ‘plays’, it is possible to orchestrate multi-machine deployments, running specific steps on all machines in the web servers group. For example, particular actions are on the database server group; then more commands are returned on the web servers group, etc.

2.Ansible tasks

Ansible is ready to execute a task once a playbook is parsed and the hosts are determined. Tasks include a name, a module reference, module arguments, and task control directives. Task execution By default, Ansible executes each task in order, one at a time, against all machines matched by the host pattern. Each task executes a module with specific arguments. You can also use the “–start-at-task <task name> flag to tell the Ansible playbook to start a playbook in the middle of a task.

3.Task execution

Each play contains a list of tasks. Tasks are executed in order, one at a time, against all machines matched by the host pattern before moving on to the next task. It is essential to understand that, within a play, all hosts will get the same task directives. This is because the purpose of a play is to map a selection of hosts to tasks. 

Interactive Ansible Architecture / Scale 03

Agentless Automation at Scale

Ansible does not require a resident agent on every managed system. The controller establishes the appropriate connection, executes automation, evaluates the result and can work across many targets in the same run.

01 / CONNECTION Agentless
02 / SCALE Parallel
03 / STATE Idempotent
04 / OPTIMIZE Efficient

Controller → Managed Nodes

The same automation model can target servers, network devices, security platforms and other resources using the connection mechanism appropriate to each target.

CONTROL NODE
ansible-playbook
Playbooks · Inventory · Variables · Collections
SSH / WINRM / API
WEB-01
Linux Server
READY
WEB-02
Linux Server
READY
RTR-01
Network Device
READY
Managed Nodes
3
Execution Mode
PARALLEL

Architecture Detail

Select a concept above to see why it matters when designing reliable automation.

01 / CONNECTION
Agentless by Design
Ansible does not require a persistent automation agent or daemon on every managed node. The controller communicates using the connection method appropriate to the target.
✓
No Ansible agent needs to remain installed and listening on the managed host.
✓
Linux and Unix systems commonly use SSH, while Windows can use WinRM and other targets can use APIs or network connection methods.
✓
This keeps the architecture controller-driven and avoids maintaining a separate resident automation service.
connection: ssh
host: web-01
transport: controller → managed node
ENGINEERING IMPACT
Agentless operation simplifies deployment because there is no separate automation agent lifecycle to maintain on each managed system.
OpenShift Networking Deep Dive

OpenShift SDN

OpenShift SDN

OpenShift SDN provides a software-defined networking model for connecting workloads across a containerised platform. Rather than treating networking as a collection of manually configured physical connections, SDN abstracts the underlying infrastructure and provides programmable mechanisms for connecting workloads, enforcing isolation, and controlling traffic between application environments.

At the centre of the model is the separation between the network control plane and the data plane. The control plane determines how connectivity and network policies should be applied, while the underlying virtual networking components handle the forwarding of traffic between workloads and nodes. This abstraction allows networking to follow the dynamic nature of container platforms, where workloads can be created, moved, scaled, and removed frequently.

A key component of traditional OpenShift SDN implementations is Open vSwitch (OVS). OVS provides virtual switching capabilities on cluster nodes and can participate in the forwarding and encapsulation mechanisms used to connect pods across the cluster. This creates a logical network that can span multiple physical or virtual hosts without requiring every workload to be directly connected to the underlying physical network.

OpenShift networking also introduces concepts such as pod networking, node networking, services, and network isolation. Pods require connectivity both within their local node and across the wider cluster, while services provide stable endpoints for applications whose underlying pods may change over time. Network policies can then be used to control which workloads are permitted to communicate with one another.

The result is a networking model designed for dynamic application environments. Workloads can scale horizontally without requiring administrators to manually configure individual network connections, while the platform can apply consistent connectivity and security policies across the cluster.

OpenShift SDN is particularly relevant to environments built around microservices and containerised applications. Applications may consist of many independently deployed components, each requiring controlled communication with other services. Software-defined networking provides the abstraction required to manage this connectivity while maintaining logical separation between applications, projects, and workloads.

Understanding OpenShift SDN therefore requires looking beyond the virtual switch itself. The important relationship is between the control plane, virtual networking layer, cluster nodes, pods, services, and network policies. Together these components form the networking fabric that allows a container platform to provide scalable and programmable application connectivity.

Interactive OpenShift Networking / Architecture 01

OpenShift SDN Architecture

Explore how OpenShift connects pods across nodes using the cluster networking layer. Select a component to see its role in the architecture — from the pod interface and OVS bridge to the overlay network and SDN control plane.

01 / WORKLOAD Pod
02 / FORWARDING Open vSwitch
03 / OVERLAY VXLAN
04 / CONTROL SDN / CNI
05 / CLUSTER Network

Cluster Networking Model

Each node participates in the cluster network. Pods connect through the node's networking stack and OVS, while the cluster networking layer provides the mechanisms required for connectivity and policy.

OPENSHIFT CLUSTER
SDN / CNI CONTROL
IPAM · topology · network policy · configuration
NODE-01
Application Node
OVS BRIDGE
forwarding / policy
POD-A
10.128.0.10
POD-B
10.128.0.11
NODE-02
Application Node
OVS BRIDGE
forwarding / policy
POD-C
10.129.0.10
POD-D
10.129.0.11
NODE-03
Application Node
OVS BRIDGE
forwarding / policy
POD-E
10.130.0.10
POD-F
10.130.0.11
OVERLAY / NODE-TO-NODE CONNECTIVITY

Component Detail

Select a component above or a node in the diagram to inspect its role in the networking architecture.

01 / WORKLOAD
Pods Are the Workloads
Pods are the workload endpoints that participate in the cluster network. Each pod receives an IP address and connects to the node networking infrastructure.
Example IP 10.128.0.10
Connectivity Pod Network
Lifecycle Ephemeral
pod: frontend-7d9f
ip: 10.128.0.10
network: cluster pod network
NETWORKING PRINCIPLE
Pod IP addresses provide connectivity inside the cluster, but applications should normally be exposed through higher level constructs such as Services and Routes rather than relying directly on an ephemeral pod address.

The Role of SDN

OpenShift SDN (Software Defined Network) is a software-defined networking solution designed to make it easier for organizations to manage their network traffic in the cloud. It is a network overlay technology that enables distributed applications to communicate over public and private networks.

OpenShift SDN is based on the Open vSwitch (OVS) platform and provides a secure, reliable, and highly available layer 3 network overlay. With OpenShift SDN, users can define their network topologies, create virtual networks, and control traffic flows between virtual machines and containers.

One of the standout features of OpenShift SDN is its ability to support multiple network isolation policies. This is carried out with the use of a network plugin. This ensures that applications can run securely without interfering with one another. Additionally, OpenShift SDN integrates seamlessly with Kubernetes, leveraging its capabilities to manage network policies, services, and routes efficientl

OpenShift Network Plugins:

OpenShift SDN uses a network plugin that provides a flexible and efficient way to connect containers across nodes in a cluster. It abstracts the underlying network complexities via the network overlay, enabling seamless communication between pods. OpenShift SDN is designed to meet the high demands of modern applications, providing secure and scalable networking solutions.

OpenShift SDN — The Container‑Native Software‑Defined Network

OpenShift SDN is Red Hat’s integrated, container‑native networking layer designed specifically for Kubernetes and OpenShift clusters. It provides pod‑to‑pod connectivity, network isolation, service routing, and policy enforcement without requiring external SDN controllers or complex overlays. Unlike traditional data‑center SDNs (ACI, NSX, Contrail), OpenShift SDN is built directly into the cluster and optimized for microservices, containers, and dynamic workloads.

What OpenShift SDN Actually Does

OpenShift SDN provides the core networking functions required for containers:

  • Pod IP allocation
  • Pod‑to‑pod connectivity across nodes
  • Service routing and load balancing
  • Network isolation between projects/namespaces
  • Policy enforcement (NetworkPolicy)
  • Integration with CNI (Container Network Interface)

It is the default SDN for many OpenShift deployments, though alternatives like OVN‑Kubernetes and Calico are also supported.

OpenShift SDN Modes — Three Networking Models

OpenShift SDN supports three operational modes, each offering different levels of isolation and flexibility.

1. Subnet Mode (Flat Network)

  • All pods can communicate freely
  • No isolation between projects
  • Simple, fast, minimal overhead
  • Best for dev/test or trusted environments

2. Multitenant Mode (Project Isolation)

  • Each project (namespace) is isolated
  • Pods in one project cannot talk to pods in another
  • Communication allowed only via services or explicit policies
  • Ideal for multi‑tenant clusters

3. NetworkPolicy Mode (Kubernetes‑Native Security)

  • Uses Kubernetes NetworkPolicy objects
  • Fine‑grained control over pod‑to‑pod communication
  • Supports Zero Trust microsegmentation
  • Most flexible and recommended for production

These modes allow OpenShift SDN to adapt to different security and operational requirements.

How OpenShift SDN Provides Pod Networking

OpenShift SDN uses an overlay network based on VXLAN to provide consistent pod IP addressing across nodes.

Key components:

  • Open vSwitch (OVS) on each node
  • VXLAN tunnels between nodes
  • OVS flows to route pod traffic
  • SDN master to manage IPAM and topology

Every node runs an OVS instance that handles:

  • pod interface creation
  • routing between pods
  • encapsulation/decapsulation
  • enforcing network policies

This creates a unified, cluster‑wide network where every pod receives a unique IP.

Security and Microsegmentation

OpenShift SDN integrates tightly with Kubernetes NetworkPolicy, enabling:

  • namespace isolation
  • pod‑level segmentation
  • Zero Trust communication patterns
  • deny‑by‑default models
  • identity‑aware microsegmentation

Policies can restrict traffic based on:

  • labels
  • namespaces
  • ports
  • protocols
  • ingress/egress rules

This makes OpenShift SDN a powerful security layer for microservices.

Service Routing and Load Balancing

OpenShift SDN integrates with kube‑proxy to provide:

  • ClusterIP services
  • NodePort services
  • LoadBalancer services
  • Ingress routing

Traffic is load‑balanced across pods using:

  • iptables (legacy)
  • IPVS (modern, scalable)

This ensures consistent service access regardless of pod location.

OpenShift SDN vs OVN‑Kubernetes

Many modern OpenShift deployments now use OVN‑Kubernetes, which provides:

  • native OVN controller
  • distributed routing
  • better scalability
  • more advanced policy enforcement
  • improved performance for large clusters

However, OpenShift SDN remains widely used due to its simplicity and stability.

Outcome — A Container‑Optimized SDN for Modern Applications

OpenShift SDN delivers:

  • simple, reliable pod networking
  • flexible isolation models
  • integrated microsegmentation
  • VXLAN‑based overlays
  • OVS‑powered forwarding
  • Kubernetes‑native policy enforcement

It is a foundational component of OpenShift’s container platform, enabling microservices, CI/CD pipelines, and cloud‑native workloads to communicate securely and efficiently.

**OpenShift Networking SDN**

1. To start OpenShift networking SDN, we have the Route constructs to provide access to specific services from the outside world. So, there is a connection point between the Route and the service construct. First, the Route connects to the service; then, the service acts as a software load balancer to the correct pod or pods with your application.

2. With the default of cluster-IP, several different service types can exist. So, you may consider the service the first level of exposing applications, but they are unrelated to DNS name resolution. To make servers accepted by FQDN, we use the OpenShift route resource, which provides the DNS.

3. The default service cluster IP addresses are from the OpenShift Dedicated internal network, which permits pods to access each other. Services are assigned an IP address and port pair that, when accessed, proxy to an appropriate backing pod.

4. By default, unsecured routes are configured and are, therefore, the easiest to configure. A secured route, however, offers security that keeps your connection private. Create secure HTTPS routes using the create route command and optionally supplying certificates and keys (PEM-format files that must be generated and signed separately).

Personal Note: Application Exposure

– When considering OpenShift and how OpenShift networking SDN works, you need to fully understand how application exposure works and how to expose yourself to the external world so that external clients can access your internal application.

– For most use cases, the applications running in Kubernetes ( see Kubernetes networking 101 ) pods’ containers need to be exposed, and this is not done with the pod IP address. Due to the fact that pods IP can be ephmemeral.

– Instead, the pods are given IP addresses for different use cases. Application exposure is done with OpenShift routes and OpenShift services. The construct used depends on the level of exposure needed. 

 

Interactive OpenShift Networking / Traffic 02

Pod Traffic & Application Exposure

Follow an application request from an external client into the OpenShift cluster. The key distinction is between DNS, the Route or Ingress layer, the Service abstraction and the actual pod endpoints behind it.

Application Traffic Path

Select a stage to inspect what happens to the request. Run the trace to watch the request progress through the logical path.

01
C
External Client
Browser / API client
→
02
DNS
DNS
Name resolution
→
03
LB
Load Balancer
Entry point
→
04
RT
Route / Ingress
Host + TLS
→
05
SVC
Service
Stable endpoint
→
06
POD
Pod
Application
SERVICE ENDPOINTS
FRONTEND-A
10.128.0.21
FRONTEND-B
10.128.0.22
FRONTEND-C
10.129.0.21
LIVE REQUEST TRACE
READY
[CLIENT] Request generated for app.network-insight.local
[DNS] Resolve application hostname
[ROUTE] Match host and forward request
[SERVICE] Select available endpoint
[POD] Deliver request to application

Traffic Detail

The selected component explains the next layer of the application networking path.

01 / CLIENT
External Client
The request begins outside the OpenShift cluster. The client normally targets a DNS name rather than a pod IP address.
Destination app.example.com
Protocol HTTPS
Target Route / Ingress
GET /api/orders
Host: app.example.com
Proto: HTTPS
ENGINEERING PRINCIPLE
Applications should normally consume stable service and route abstractions rather than depending directly on ephemeral pod addresses.

OpenShift SDN

Kuberenetes Concepts

Kubernetes’ concept of a POD

As the smallest compute unit that can be defined, deployed, and managed, OpenShift leverages the Kubernetes concept of a pod. There is one or more containers deployed on one host. A pod is the equivalent of a physical or virtual machine instance to a container. Containers within pods can share their local storage and networking, as each pod has its IP address.

An individual pod has a lifecycle; it is defined, assigned to a node, and then runs until the container(s) exit or are removed for some other reason. Pods can be removed after exiting or retained to allow access to container logs, depending on policy and exit code.

In OpenShift, pod definitions are largely immutable; they cannot be modified while running. Changes are implemented by terminating existing pods and recreating them with modified configurations, base images, or both. Additionally, pods are expendable and do not maintain their state when recreated. In general, pods should not be managed directly by users but by higher-level controllers.

Kubernetes PODKubernetes’ Concept of Services

Kubernetes services act as internal load balancers. They identify a set of replicated pods to proxy connections to them. While the service remains consistently available, backing pods can be added or removed arbitrarily, enabling everything that depends on them to refer to them at a consistent address. This is a key abstraction layer a a Kubernetes design. Additionally, the OpenShift Container Platform uses cluster IP addresses to allow pods to communicate with each other and access the internal network.

Note: The service can be assigned additional external IP and ingress IP addresses outside the cluster to allow external access. An external IP address can also be a virtual IP address that provides highly available access to the service.

Services are assigned IP addressing and port mappings, which proxy to an appropriate backing pod when accessed. Using a label selector, a service can find all containers running on a specific port that provides a particular network service. Like pods, services are REST objects. 

Kubernetes service

There are a couple of options for getting hands-on with OpenShift. You can download the CodeReady Containers for Linux, Microsoft, and MacOS or RedHat’s pre-built Sandbox Lab environment.

Configuring OpenShift Cluster

### Getting Started: Setting Up Your OpenShift Environment

Before diving into configuration, it’s essential to set up your OpenShift environment correctly. Start by installing the OpenShift CLI (oc) and setting up access to your cluster. For beginners, Red Hat offers OpenShift Online, which provides a managed cloud service to ease you into the ecosystem. Ensure your local development environment is compatible and includes the necessary tools such as Docker and Kubernetes.

—

### Configuring Your OpenShift Cluster: Step-by-Step

1. **Networking:** Begin by configuring the cluster networking. OpenShift uses a Software-Defined Network (SDN) to manage communication between pods. Choose a suitable network plugin, like OVN-Kubernetes or Calico, and configure the network policies to secure your application traffic.

2. **Storage:** Proper storage configuration is vital for stateful applications. OpenShift supports various storage solutions, including Persistent Volumes (PVs) and Persistent Volume Claims (PVCs). Evaluate your storage needs and select appropriate storage classes to ensure data persistence and redundancy.

3. **Security:** OpenShift offers robust security features. Configure role-based access control (RBAC) to restrict user permissions and define security contexts for your pods. Implement network security policies and enable cluster auditing to monitor and respond to potential threats.

4. **Resource Management:** Efficient resource allocation ensures optimal performance. Configure resource quotas and limits to prevent overconsumption. Utilize Horizontal Pod Autoscalers (HPAs) to automatically adjust the number of pods based on current demand.

—

### Advanced Configuration: Enhancing Your Cluster

For those looking to further optimize their OpenShift cluster, consider implementing advanced configurations:

– **Monitoring and Logging:** Deploy tools like Prometheus and Grafana for monitoring, and Elasticsearch, Fluentd, and Kibana (EFK) stack for logging. These tools offer insight into cluster performance and help troubleshoot issues quickly.

– **CI/CD Integration:** Integrate OpenShift with your existing CI/CD pipeline using tools like Jenkins or GitLab. This integration allows for automated builds, tests, and deployments, enhancing your development workflow.

– **Custom Operators:** Leverage OpenShift’s Operator Framework to automate the management of complex applications. Operators can significantly reduce manual intervention and streamline application lifecycle management.

OpenShift Networking Deep Dive

### The Basics of OpenShift Networking

OpenShift networking is built on the foundation of Kubernetes, but with enhancements that cater to enterprise needs. At its core, OpenShift uses an overlay network to manage communication between pods across nodes. This network ensures seamless connectivity and scalability, allowing applications to function efficiently in a distributed environment. By default, OpenShift uses Open vSwitch (OVS) as its software-defined networking (SDN) solution, which provides flexibility and control over network traffic.

### Key Components of OpenShift Networking

Understanding the components that make up OpenShift networking is essential for effective network management. The primary components include:

1. **Pods and Services:** Pods are the smallest deployable units in OpenShift, and services provide a stable endpoint for accessing these pods.

2. **Ingress and Egress Traffic:** OpenShift manages ingress traffic via routes, which define how external users access the services. Egress traffic, on the other hand, involves controlling the outbound connections from the pods to external services.

3. **Network Policies:** These policies define the rules for communication between pods. By implementing network policies, you can enhance security by restricting the flow of traffic between different parts of your application.

### Advanced Networking Features

OpenShift offers advanced networking features that cater to specific enterprise needs. Some of these features include:

– **Multi-Cluster Networking:** OpenShift supports the deployment of applications across multiple clusters, enabling organizations to achieve high availability and disaster recovery.

– **Service Mesh Integration:** With the rise of microservices, service mesh solutions like Istio are integrated into OpenShift to manage service-to-service communication, providing observability, traffic management, and security.

– **Custom DNS and Load Balancing:** OpenShift allows for custom DNS configurations and integrates with various load balancers to distribute traffic efficiently across nodes.

Service Discovery and DNS

Applications depend on each other to deliver information to users. These relationships are complex to manage in an application spanning multiple independently scalable pods. So, we don’t access applications by pod IP. These IP addresses will change for one reason, and it’s not a scalable solution.

To make this easier, OpenShift deploys DNS when the cluster is deployed and makes it available on the pod network. DNS in OpenShift allows pods to discover the resources in the OpenShift SDN.

Layer Approach to DNS.

DNS in the Openshift is a layered approach. Originally, DNS in Kubernetes was used for service discovery. The problem was solved a long time ago. DNS was the answer for service discovery back then, and it still is. Service Discovery means an application or service inside; it can reference a service by name, not an IP address.

The deployed pods represent microservices. A Kubernetes service points to these pods and discovers them by DNS name, so the service is transparent. The internal DNS manages this in Kubernetes; originally, it was SKYDNS KubeDNS, and now it’s Core DNS.

The DNS Operator

The DNS operator runs DNS services and uses Core DNS. The pods use the internal Core DNS server for DNS resolution. The pod’s DNS name server is automatically set to the Core DNS. OpenShift provides its internal DNS, implemented via Core DNS and dnsmasq for service discovery. The dnsmasq is a lightweight DNS forwarder that provides DNS. 

The DNS Operator has several roles:

    1. It creates the default cluster DNS name cluster. local
    2. Assigns DNS names to namespaces. The namespace is part of the FQDN.
    3. Assign DNS names to services. So, both the service and namespace are part of the FQDN name.
OpenShift DNS Operator
Diagram: OpenShift DNS Operator. Source OpenShift Docs.

OpenShift SDN and the DNS processes

**The Controller nodes

The OpenShift cluster network has several components. First, we have a controller node. There are multiple controller nodes in a cluster. The controller nodes redirect traffic to the PODs. We run a route on each controller node and use Core DNS. So, in front of the Kubernetes cluster or layer, this is a hardware load balancer. Then, we have external DNS, which is outside of the cluster. 

This external DNS has a wildcard domain; thus, external DNS is resolved to the frontend hardware load balancer through the wildcard. So, users who want to access a service issue the request and contact external DNS for name resolution.

Then, external resolves the wildcard domain to the load balancer, which will load balance to the different control nodes. For these control nodes, we can address the route and service.

**OpenShift and DNS: Wildcard DNS.

OpenShift has an internal DNS server that is reachable only by Pods. We need an external DNS server configured with wildcard DNS to make the service available by name to the outside. The wildcard DNS is resolved to all resources created in the cluster domain by fixing the OpenShift load balancer. 

This OpenShift load balancer provides a frontend to the control nodes run as ingress controllers and part of the cluster. They have access to internal resources and are part of the internal cluster.

**OpenShift ingress operators

For this to work, we need to use the OpenShift Operators. The Ingress Operator implements the IngressController API and enables external access to OpenShift Container Platform cluster services. It does this by deploying one or more HAProxy ingress controllers to handle the routing side.

You can use the Ingress Operator to route traffic by specifying the OpenShift Container Platform route construct. You may also have heard of the Kubernetes Ingress resources. Both are similar, but the OpenShift route can have additional security features and the use case of split green deployments.

The OpenShift Route Construct and Encryption

The OpenShift Container Platform route provides traffic to services in the cluster. Routes also offer advanced features that might not be supported by standard Kubernetes Ingress Controllers, such as TLS re-encryption, TLS passthrough, and split traffic for blue-green deployments.

In Kubernetes’s words, we use Ingress, which exposes services to the external world. However, in Openshift, it is best practice to use a routing set, which is an alternative to Ingress.

We have three Pods, each with a different IP address. To access these Pods, we need a service. Essentially, this service provides a load balancing service and distributes load to the pods using a load balancing algorithm, a round robin by default.

The service is an internal component, and in Openshift, we have routes that provide a URL for the services so they can be accessible from the outside world. So, the URL created by the Route points to the service, and the service points to the pods. In the Kubernetes world, Ingress pointed out the benefits, not routes.

Different types of services

Type: 

  • ClusterIP: The Service is exposed as an IP address internal to the cluster. This is useful for a microservices design where the front end connects to the backend without exposing the service externally. These are the Default Types. The service type is ClusterIP, meaning you have a cluster-wide IP address.
  • Node Port: This is a service type that exposes a port on the node’s IP address. This is like port forwarding on the physical node. However, the node port does not connect the internal cluster pods to a port dynamically exposed on the physical node. So external users can connect to the port on the node, and we get the port forwarded to the node port. This then goes to the pods and is load-balanced to the pods.
  • Load Balancer: You can find a service type in public cloud environments. 

Forming the network topology

OpenShift SDN networking

New pod creation: OpenShift networking SDN

As new pods are created on a host, the local OpenShift software-defined network (SDN) allocates and assigns an IP address from the cluster network subnet assigned to the node and connects the veth interface to a port in the br0 switch. It does this with the OpenShift OVS, which programs OVS rules via the OVS bridge. At the same time, the OpenShift SDN injects new OpenFlow entries into the OVSDB of br0 to Route traffic addressed to the newly allocated IP Address to the correct OVS port connecting the pod.

Pod network: 10.128.0.0/14

The pod network defaults to use the 10.128.0.0/14 IP address block. Each node in the cluster is assigned a /23 CIDR IP address range from the pod network block. That means, by default, each application node in OpenShift can accommodate a maximum of 512 pods. 

OpenFlow manages how IP addresses are allocated to each application node. The primary CNI plugin, the essence of SDN for Openshift, establishes the cluster-wide network and configures the overlay network using the OVS.

OpenShift CNI SDN Plugin

OVS is used in your OpenShift cluster as the communications backbone for your deployed pods. OVS in and out of every pod affects traffic in and out of the OpenShift cluster. OVS runs as a service on each node in the cluster. The Primary CNI SDN Plugin uses network policies using Openvswitch flow rules, which dictate which packets are allowed or denied. 

Configuring OpenShift Networking SDN

When you deploy OpenShift, the default configuration for the pod network’s topology is a single flat network. Every pod in every project can communicate without restrictions. OpenShift SDN uses a plugin architecture that provides different network topologies. Depending on your network and security requirements, you can choose a plugin that matches your desired topology. Currently, three OpenShift SDN plugins can be enabled in the OpenShift configuration without significantly changing your cluster.

OpenShift SDN default CNI network provider

OpenShift Container Platform uses a software-defined networking (SDN) approach to provide a unified cluster network that enables communication between pods across the OpenShift Container Platform cluster. The OpenShift SDN establishes and maintains this pod network, configuring an overlay network using Open vSwitch (OVS).

OpenShift SDN modes:

OpenShift SDN provides three SDN modes for configuring the pod network.

  1. ovs-subnet— Enabled by default. Creates a flat pod network, allowing all project pods to communicate.
  2. ovs-multitenant—Separates the pods by project. Applications deployed in a project can only communicate with pods deployed in the same project. 
  3. ovs-network policy: This plugin provides fine-grained Ingress and egress rules for applications. It can be more complex than the other two.
    • OpenShift ovs-subnet

The OpenShift ovs-subnet is the original OpenShift SDN plugin. This plugin provides basic connectivity for the Pods. This network connectivity is sometimes called a “flat” pod network. It is described as a “flat” Pod network because there are no filters or restrictions, and every pod can communicate with every other Pod and Service in the cluster. Flat network topology for all pods in all projects lets all deployed applications communicate. 

    • OpenShift ovs-multitenant

With OpenShift ovs-multitenant plugin, each project receives a unique VXLAN ID known as a Virtual Network ID (VNID). All the pods and services of an OpenShift Project are assigned to the corresponding VNID. So now we have segmentation based on the VNID. Doing this maintains project-level traffic isolation, meaning that Pods and Services of one Project can only communicate with Pods and Services in the same project. There is no way for Pods or Services from one project to send traffic to another. The ovs-multitenant plugin is perfect if just having projects separated is enough.

Unique across projects

Unlike the ovs-subnet plugin, which passes all traffic across all pods, this one assigns the same VNID to all pods for each project, keeping them unique across projects. It also sets up flow rules on the br0 bridge to ensure that traffic is only allowed between pods with the same VNID.

VNID for each Project

When the ovs-multitenant plugin is enabled, each project is assigned a VNID. The VNID for each Project is maintained in the etcd database on the OpenShift master node. When a pod is created, its linked veth interface is associated with its Project’s VNID, and OpenFlow rules are made to ensure it can communicate only with pods in the same project.

    • The ovs-network policy plugin 

The ovs-multitenant plugin cannot control access at a more granular level. This is where the Ovs-network policy plugin steps in, add more configuration power and lets you create custom NetworkPolicy objects. As a result, the ovs-network policy plugin provides fine-grained access control for individual applications, regardless of their project. By isolating policy using network policy objects, you can tailor your topology requirement.

This is the Kubernetes Network Policy, so you map, Label, or tag your application, then define a network policy definition to allow or deny connectivity across your application. Network policy mode will enable you to configure their isolation policies using NetworkPolicy objects. Network policy is the default mode in OpenShift Container Platform 4.8.

  • OpenShift OVN Kubernetes CNI network provider

OpenShift Container Platform uses a virtualized network for pod and service networks. The OVN-Kubernetes Container Network Interface (CNI) plugin is a network provider for the default cluster network. OVN-Kubernetes is based on the Open Virtual Network (OVN) and provides an overlay-based networking implementation. A cluster that uses the OVN-Kubernetes network provider also runs Open vSwitch (OVS) on each node. OVN configures OVS on each node to implement the declared network configuration.

OVN-Kubernetes features

The OVN-Kubernetes Container Network Interface (CNI) cluster network provider implements the following features:

  • Uses OVN (Open Virtual Network) to manage network traffic flows. OVN is a community-developed, vendor-agnostic network virtualization solution.
  • Implements Kubernetes network policy support, including ingress and egress rules.
  • It uses the Geneve (Generic Network Virtualization Encapsulation) protocol rather than VXLAN to create an overlay network between nodes.
Interactive OpenShift Networking / Security 03

Network Isolation & Overlay Modes

OpenShift networking can be viewed through several generations of networking models. Explore the historical OpenShift SDN modes and then compare them with the modern OVN-Kubernetes architecture.

HISTORICAL / OPENSHIFT SDN
ovs-subnet
ovs-multitenant
NetworkPolicy
MODERN / OVN-KUBERNETES

Select a Networking Model

Select each model to see how connectivity and isolation change. The first three represent the OpenShift SDN model described in the article; OVN-Kubernetes represents the newer networking architecture.

EXAMPLE TRAFFIC MATRIX
SOURCE → DEST
SAME PROJECT
OTHER PROJECT
POLICY MATCH
frontend → backend
ALLOW
—
—
app → database
ALLOW
—
—

Networking Model Detail

The selected model changes the way connectivity and policy are represented across the cluster.

HISTORICAL / OPENSHIFT SDN
ovs-subnet — Flat Network
Pods across the cluster can communicate using the shared pod network. This provides simple connectivity but does not provide the same project-level isolation as more restrictive networking models.
Isolation Low
Connectivity Broad
Model Shared subnet
mode: ovs-subnet
network: shared pod network
isolation: minimal
ENGINEERING PRINCIPLE
Connectivity and isolation are different design goals. A flat network can provide reachability, but security policy is required when workloads should not freely communicate.
OVERLAY & POLICY COMPARISON
OpenShift SDN Historical OVS-based model
Overlay VXLAN in the historical SDN model
Policy NetworkPolicy / isolation
OVN-Kubernetes Modern networking architecture
Overlay Geneve-based networking
Policy Distributed policy enforcement
Chaos Engineering

Chaos Engineering Kubernetes

Chaos Engineering Kuberentes

In Kubernetes environments, reliability cannot be measured simply by whether workloads are running. Distributed applications can remain technically available while experiencing increased latency, elevated error rates, resource exhaustion, or degraded dependencies. Chaos Engineering provides a controlled way to expose these weaknesses before they become production incidents.

Chaos Engineering involves deliberately introducing controlled failures or abnormal conditions into a system and observing how it responds. The objective is not to cause disruption for its own sake, but to validate resilience assumptions, identify unknown failure modes, and improve the system’s ability to recover. In Kubernetes, experiments can target workloads, nodes, networking, storage, dependencies, or resource availability.

A successful experiment begins with a hypothesis. Engineers define what they expect to happen if a particular component fails and establish the signals that will demonstrate whether the system remained within acceptable limits. A controlled fault is then introduced while telemetry is collected, allowing the resulting behaviour to be compared with the original reliability expectations.

This makes metrics and observability fundamental to Chaos Engineering. Key signals can include request latency, error rates, throughput, CPU and memory utilisation, pod restarts, resource saturation, queue depth, and application-specific service-level indicators. Looking at these signals together provides a much clearer picture of system health than relying on a single metric.

Latency metrics help reveal how a failure affects application response times. Introducing network delay, dependency latency, or resource contention can demonstrate whether timeouts, retries, load balancing, and application fallbacks behave as expected. A service may remain available while its latency increases enough to create a significant user-facing impact.

Error and availability metrics show whether the system continues to meet its reliability objectives when components fail. Controlled pod termination, dependency failures, or network disruption can reveal whether Kubernetes workloads restart correctly and whether applications have sufficient redundancy and graceful failure mechanisms.

Resource and saturation metrics provide another important perspective. CPU throttling, memory pressure, constrained capacity, or excessive workload demand can expose weak resource limits, inefficient scheduling, or scaling assumptions. These experiments can help validate whether Kubernetes workloads respond correctly when the underlying resources become constrained.

The real value of Chaos Engineering comes from connecting these experiments to observable outcomes. A useful experiment should establish a hypothesis, introduce a controlled fault, monitor the relevant telemetry, measure the impact, and capture what was learned. The results can then feed back into application design, Kubernetes configuration, alerting, capacity planning, and incident response.

Chaos Engineering therefore becomes more than deliberately creating failures. It is a practical method for testing whether a Kubernetes platform and the applications running on it can detect, tolerate, and recover from unexpected conditionsbefore those conditions become real-world incidents.

Interactive Chaos Engineering / Experiment 01

Chaos Engineering Experiment Loop

Chaos engineering is not simply breaking a system. The engineering process is to define normal behavior, form a hypothesis, introduce a realistic failure, observe the system, and use the evidence to improve resilience.

Experiment Objective
Prove that the application can tolerate failure
Start with measurable steady-state behavior. Then introduce one controlled fault and determine whether the application remains within its reliability objectives or successfully recovers.
p99 < 200ms
Errors < 1%
Availability > 99.9%
CPU < 70%
Engineering Principle
Test the hypothesis
A useful experiment attempts to disprove an assumption about system resilience rather than simply generate failure.
Experiment Lifecycle
01
Steady State
Establish measurable normal behavior.
02
Hypothesis
Predict how the system should respond.
03
Inject Failure
Introduce a controlled realistic fault.
04
Observe
Measure system behavior during failure.
05
Compare
Compare results against steady state.
06
Improve
Fix weaknesses and repeat the experiment.
Selected Stage
01 — Steady State
BASELINE
Before introducing chaos, define what healthy behavior looks like. The baseline should be measurable so that the experiment has something meaningful to compare against.
Question: What does “healthy” look like for this application?
Engineering principle: If you cannot measure steady state, you cannot reliably determine whether a chaos experiment caused meaningful degradation.
Experiment Telemetry
p99 Latency
142 ms
Error Rate
0.3%
Availability
99.98%
CPU Usage
54%
Experiment Result
HYPOTHESIS SUPPORTED
The simulated failure produced a measurable change, but the service recovered and returned toward its defined steady state.
Engineering Takeaway: Chaos engineering should be treated as a controlled scientific experiment. Define the steady state first, introduce a realistic failure with a known blast radius, observe the system, compare the result with the baseline, and feed the learning back into the design.
STEADY STATE → HYPOTHESIS → FAILURE → OBSERVE → COMPARE → IMPROVE → REPEAT

Highlights: Chaos Engineering Kuberentes

The Principles of Chaos Engineering

Chaos Engineering is grounded in several core principles. First, it is about embracing failure as a learning opportunity. By simulating failures, teams can understand how their systems react under stress. Another principle is to conduct experiments in a controlled environment, ensuring any disruptions do not affect end users. Lastly, Chaos Engineering emphasizes continuous learning and improvement, turning insights from experiments into actionable changes.

**Tools and Techniques**

Implementing chaos engineering requires a robust set of tools and techniques to simulate real-world disruptions effectively. Some popular tools in the chaos engineering toolkit include Chaos Monkey, Gremlin, and Litmus, each offering unique features to test system resilience. These tools enable engineers to automate experiments, analyze results, and derive actionable insights. By using these advanced tools, organizations can run experiments at scale and gain confidence in their systems’ ability to handle unpredictable events.

**Why Chaos Engineering Matters**

Modern systems are complex and often distributed across multiple platforms and services. This complexity makes it difficult to predict how systems will behave under unexpected conditions. Chaos Engineering allows teams to identify hidden vulnerabilities, improve system reliability, and enhance user experience. Companies like Netflix and Amazon have successfully used Chaos Engineering to maintain their competitive edge by ensuring their services remain robust and available.

**The Traditional Application**

When considering Chaos Engineering kubernetes, we must start from the beginning. Not too long ago, applications ran in single private data centers, potentially two data centers for high availability. These data centers were on-premises, and all components were housed internally. Life was easy, and troubleshooting and monitoring any issues could be done by a single team, if not a single person, with predefined dashboards. Failures were known, and there was a capacity planning strategy that did not change too much, and you could carry out standard dropped packet test

**A Static Infrastructure**

The network and infrastructure had fixed perimeters and were pretty static. There weren’t many changes to the stack, for example, daily. Agility was at an all-time low, but that did not matter for the environments in which the application and infrastructure were housed. However, nowadays, we are in a completely different environment with many more moving parts with an increasing need to support a reliable distributed system.

Complexity is at an all-time high, and business agility is critical. Now, we have distributed applications with components/services located in many different places and types of places, on-premises and in the cloud, with dependencies on both local and remote services. So, in this land of complexity, we must find system reliability. A reliable system is one that you can trust will be reliable.

Chaos Engineering in Kubernetes — Designing for Failure in Cloud‑Native Systems

Chaos engineering in Kubernetes is the discipline of intentionally injecting failure into clusters to validate resilience, uncover hidden weaknesses, and ensure workloads behave correctly under stress. Instead of waiting for outages, chaos engineering forces the system to experience them in a controlled, observable way. Kubernetes is a perfect environment for chaos experiments because it is inherently dynamic, distributed, and built around self‑healing primitives.

Why Chaos Engineering Matters in Kubernetes

Kubernetes abstracts infrastructure, but abstraction hides failure. Chaos engineering exposes how workloads behave when:

  • nodes crash
  • pods restart unexpectedly
  • network latency spikes
  • storage becomes slow or unavailable
  • API server becomes overloaded
  • autoscaling triggers aggressively
  • controllers misbehave

Chaos engineering ensures your cluster is resilient by design, not by assumption.

Core Principles of Kubernetes Chaos Engineering

Chaos engineering in Kubernetes follows four foundational principles:

1. Define Steady State

You must know what “normal” looks like.

Examples:

  • p99 latency under 200ms
  • error rate < 1%
  • CPU usage < 70%
  • pods healthy and ready

Steady state becomes the baseline for evaluating chaos impact.

2. Introduce Realistic Failure

Chaos experiments should mimic real‑world failures:

  • node shutdown
  • pod eviction
  • network partition
  • DNS failure
  • container crash loops
  • storage throttling

Chaos must be representative, not random.

3. Observe System Behavior

Use Kubernetes observability tools:

  • Prometheus metrics
  • Loki logs
  • Jaeger/Tempo traces
  • Kiali (service mesh)
  • OpenTelemetry pipelines

Chaos without observability is just destruction.

4. Improve and Repeat

Chaos engineering is iterative.

Each experiment reveals weaknesses that must be fixed, validated, and re‑tested.

Common Chaos Experiments in Kubernetes

Here are the most valuable chaos tests for real‑world clusters:

1. Pod Kill / Pod Restart

Simulates container crashes.

Validates:

  • readiness/liveness probes
  • retry logic
  • graceful shutdown
  • autoscaling behavior

2. Node Failure / Node Drain

Simulates hardware failure or maintenance.

Validates:

  • pod rescheduling
  • replica distribution
  • disruption budgets
  • cluster autoscaler behavior

3. Network Latency / Packet Loss

Simulates degraded network conditions.

Validates:

  • microservices resilience
  • timeout handling
  • circuit breakers
  • service mesh routing

4. API Server Stress

Simulates control‑plane overload.

Validates:

  • controller stability
  • admission webhook behavior
  • cluster responsiveness

5. Storage Throttling / Volume Detach

Simulates slow or failing storage.

Validates:

  • database failover
  • persistent volume resilience
  • application retry logic

6. Service Mesh Failure

Simulates sidecar or proxy issues.

Validates:

  • fallback routing
  • mesh control‑plane resilience
  • degraded mode behavior

Chaos Engineering + Kubernetes Operators

Operators automate application lifecycle management.

Chaos engineering validates operator logic:

  • reconciliation loops
  • state recovery
  • CRD consistency
  • failover automation

If an operator cannot recover state after chaos, it is not production‑ready.

Chaos Engineering for Security

Chaos engineering can also test security controls:

  • RBAC misconfigurations
  • network policy gaps
  • pod escape attempts
  • privilege escalation
  • secret rotation failures

Security chaos engineering ensures Zero Trust principles hold under stress.

Tools for Kubernetes Chaos Engineering

Popular tools include:

  • LitmusChaos — CNCF project for Kubernetes chaos workflows
  • Chaos Mesh — powerful, Kubernetes‑native chaos platform
  • Gremlin — enterprise chaos engineering suite
  • Kube‑Monkey — pod‑kill chaos inspired by Netflix’s Chaos Monkey

These tools integrate with CI/CD pipelines, allowing chaos tests to run automatically.

Outcome — A Resilient, Self‑Healing Kubernetes Platform

Chaos engineering ensures Kubernetes workloads:

  • survive node failures
  • recover from pod crashes
  • tolerate network degradation
  • handle storage instability
  • maintain service reliability
  • enforce security under stress

Chaos engineering transforms Kubernetes from “works in ideal conditions” to “works under real‑world failure.”

Implementing Chaos Engineering

Implementing Chaos Engineering involves several steps. First, start by defining a “steady state” of your system’s normal operations. Next, develop hypotheses about potential failure points and design experiments to test these. Use tools like Chaos Monkey or Gremlin to introduce controlled disruptions and observe the outcomes. Finally, analyze the results and apply any necessary changes to improve system resilience.

Challenges and Considerations

While Chaos Engineering offers many benefits, it also presents challenges. Resistance from stakeholders who are wary of intentional disruptions can be a hurdle. Additionally, ensuring that experiments are conducted safely and do not impact customers is crucial. To overcome these challenges, it’s important to foster a culture of transparency, continuous learning, and collaboration among teams.

Benefits of Chaos Engineering in Kubernetes:

1. Enhanced Reliability: By subjecting Kubernetes deployments to controlled failures, Chaos Engineering helps organizations identify weak points and vulnerabilities, enabling them to build more resilient systems that can withstand unforeseen events.

2. Improved Incident Response: Chaos Engineering allows organizations to test and refine their incident response processes by simulating real-world failures. This helps teams understand how to quickly detect and mitigate potential issues, reducing downtime and improving the overall incident response capabilities.

3. Cost Optimization: Chaos engineering can help optimize resource utilization within a Kubernetes cluster by identifying and addressing performance bottlenecks and inefficient resource allocation. This, in turn, leads to cost savings and improved efficiency.

Personal Note: Today’s standard explanation for Chaos Engineering is “The facilitation of experiments to uncover systemic weaknesses.” The following is true for Chaos Engineering.

  1. Begin by defining “steady state” as some measurable output of a system that indicates normal behavior.
  2. Hypothesize that this steady state will persist in both the control and experimental groups.
  3. Submit variables that mirror real-world events like servers that crash, hard drives that malfunction, severed network connections, etc.
  4. Then, as a final step, try to disprove the hypothesis by looking for a steady-state difference between the control and experimental groups.

Chaos Engineering Scenarios in Kubernetes:

Chaos Engineering in Kubernetes involves deliberately introducing failures into the system to observe how it behaves and recovers. This proactive approach enables teams to understand the system’s response to unexpected disruptions, whether it’s a pod failure, network latency, or a node shutdown.

**Implementing Chaos Experiments in Kubernetes**

To harness the full potential of Chaos Engineering, it’s essential to implement chaos experiments thoughtfully. Start by identifying critical components of your Kubernetes cluster that need testing. Tools like LitmusChaos and Chaos Mesh provide a framework for defining and executing chaos experiments. These experiments simulate disruptions such as pod deletions, CPU stress, and network partitioning, allowing teams to evaluate system behavior and improve fault tolerance.

**Chaos Engineering Kubernetes**

1. Pod Failures: Simulating failures of individual pods within a Kubernetes cluster allows organizations to evaluate how the system responds to such events. By randomly terminating pods, Chaos Engineering can help ensure that the system can handle pod failures gracefully, redistributing workload and maintaining high availability.

2. Network Partitioning: Introducing network partitioning scenarios can help assess the resilience of a Kubernetes cluster. By isolating specific nodes or network segments, Chaos Engineering enables organizations to test how the group reacts to network disruptions and evaluate the effectiveness of load balancing and failover mechanisms.

3. Resource Starvation: Chaos Engineering can simulate resource scarcity scenarios by intentionally consuming excessive resources, such as CPU or memory, within a Kubernetes cluster. This allows organizations to identify potential performance bottlenecks and optimize resource allocation strategies.

Understanding GKE-Native Monitoring

GKE-Native Monitoring is a comprehensive solution designed for Google Kubernetes Engine (GKE). It empowers developers and operators with real-time insights into the health and performance of their Kubernetes clusters, pods, and containers. Leveraging the power of Prometheus, GKE-Native Monitoring offers a wide range of monitoring metrics, such as CPU and memory utilization, network traffic, and custom application metrics.

GKE-Native Monitoring: Features

GKE-Native Monitoring boasts several key features that enhance observability and troubleshooting capabilities. One notable feature is the ability to create custom dashboards, enabling users to visualize and analyze critical metrics tailored to their needs. GKE-Native Monitoring integrates seamlessly with Stackdriver, Google Cloud’s unified monitoring and logging platform, allowing for centralized management and alerting across multiple GKE clusters.

The Rise of Chaos Engineering

Infrastructure is becoming increasingly complex, and let’s face it, a lot can go wrong. It’s imperative to have a global view of all the infrastructure components and a good understanding of the application’s performance and health. In a large-scale container-based application design, there are many moving pieces and parts, and trying to validate the health of each piece manually is hard. 

With these new environments, especially cloud-native at scale. Complexity is at its highest, and many more things can go wrong. For this reason, you must prepare as much as possible so the impact on users is minimal.

So, the dynamic deployment patterns you get with frameworks with Kubernetes allow you to build better applications. But you need to be able to examine the environment and see if it is working as expected. Most importantly, this course’s focus is that to prepare effectively, you need to implement a solid strategy for monitoring in production environments.

Chaos Engineering Kubernetes

For this, you need to understand practices and tools like Chaos Engineering and how they can improve the reliability of the overall system. Chaos Engineering is the ability to perform tests in a controlled way. Essentially, we intentionally break things to learn how to build more resilient systems.

So, we are injecting faults in a controlled way to make the overall application more resilient by injecting various issues and faults. It comes down to a trade-off and your willingness to accept it. There is a considerable trade-off with distributed computing. You have to monitor efficiently, have performance management, and, more importantly, accurately test the distributed system in a controlled manner. 

Service Mesh Chaos Engineering

 Service Mesh is an option to use to implement Chaos Engineering. You can also implement Chaos Engineering with Chaos Mesh, a cloud-native Chaos Engineering platform that orchestrates tests in the Kubernetes environment. The Chaos Mesh project offers a rich selection of experiment types. Here are the choices, such as the POD lifecycle test, network test, Linux kernel, I/O test, and many other stress tests.

Implementing practices like Chaos Engineering will help you understand and manage unexpected failures and performance degradation. The purpose of Chaos Engineering is to build more robust and resilient systems. 

Cloud Service Mesh:

### The Role of Cloud Service Mesh in Modern Architecture

Traditional monolithic applications are being replaced by microservices architectures, which break down applications into smaller, independent services. While this shift offers numerous benefits such as scalability and flexibility, it also introduces complexities in managing the interactions between these services. This is where a Cloud Service Mesh comes into play. It provides a structured method to handle service discovery, load balancing, failure recovery, metrics, and monitoring without adding heavy burdens to the developers.

### Google’s Pioneering Efforts in Cloud Service Mesh

Google has been at the forefront of cloud innovation, and its contributions to the development of Cloud Service Mesh technologies are no exception. Through its open-source project Istio, Google has provided a robust framework for managing microservices.

Istio simplifies the observability, security, and management of microservices, making it easier for enterprises to adopt and scale their cloud-native applications. By integrating Istio with their Google Kubernetes Engine (GKE), Google has made it seamless for organizations to deploy and manage their service meshes.

### Enhancing Reliability with Chaos Engineering

One of the most intriguing aspects of utilizing a Cloud Service Mesh is its ability to enhance system reliability through Chaos Engineering. This practice involves intentionally injecting faults into the system to test its resilience and identify potential weaknesses.

By simulating real-world failures, organizations can better prepare for unexpected issues and ensure their services remain robust under pressure. The granular control provided by a Cloud Service Mesh makes it an ideal platform for implementing Chaos Engineering practices, helping to create more resilient and dependable cloud environments.

 

Interactive Chaos Engineering / Kubernetes Failure Lab

Kubernetes Failure Injection

Select a realistic failure scenario and trace what should happen next: failure injection, Kubernetes response, application impact and recovery. The goal is not to break Kubernetes randomly — it is to test a resilience hypothesis.

Selected Experiment
Pod Failure
Terminate one application pod and observe whether the Service continues directing traffic to healthy replicas while the Deployment recreates the failed workload.
◆
Application
→
×
Pod Fails
→
↻
Replica Recovery
→
✓
Service Restored
Failure Injected Application pod termination
Kubernetes Reaction Controller recreates the desired replica
What To Observe Endpoint health, pod readiness and request errors
Recovery Signal Healthy replica returns to service
Potential Impact: A correctly configured replicated application should experience little or no user-visible disruption.
Experiment Console
chaos-engineering / kubernetes
$ experiment --scenario pod-failure
[baseline] replicas=3 ready=3 error_rate=0.3%
[inject] terminating pod/frontend-7d8c9
[event] pod state=Terminating
[controller] desired replicas=3
[scheduler] replacement pod created
[recovery] replicas=3 ready=3
Experiment Complete
The simulated failure was introduced and the recovery path completed. Check whether the application remained inside its reliability objectives.
Engineering Takeaway: Each chaos experiment should connect a realistic failure to an expected Kubernetes response and a measurable recovery signal. The question is not simply “did something break?” — it is “did the platform and application recover the way we designed them to?”

Chaos Engineering Kuberentes

Monitoring & Troubleshooting 

Beyond the Complexity Horizon

Therefore, monitoring and troubleshooting are much more demanding, as everything is interconnected, making it difficult for a single person in one team to understand what is happening entirely. The edge of the network and application boundary surpasses one location and team. Enterprise systems have gone beyond the complexity horizon, and you can’t understand every bit of every single system.

Even if you are a developer closely related to the system and truly understand the nuts and bolts of the application and its code, no one can understand every bit of every single system.  So, finding the correct information is essential, but once you see it, you have to give it to those who can fix it. So monitoring is not just about finding out what is wrong; it needs to alert, and these alerts need to be actionable.

Troubleshooting: Chaos engineering kubernetes

– Chaos Engineering aims to improve a system’s reliability by ensuring it can withstand turbulent conditions. Chaos Engineering makes Kubernetes more secure. So, if you are adopting Kubernetes, you should adopt Chaos Engineering as an integral part of your monitoring and troubleshooting strategy.

– Firstly, we can pinpoint the application errors and understand, at best, how these errors arose. This could be anything from badly ordered scripts on a web page to a database query that has bad sequel calls or even unoptimized code-level issues.

– Or there could be something more fundamental going on. It is expected to have issues with how something is packaged into a container. You can pull in the incorrect libraries or even use a debug version of the container.

– Or there could be nothing wrong with the packaging and containerization of the container; it is all about where the container is being deployed. There could be something wrong with the infrastructure, either a physical or logical problem—incorrect configuration or a hardware fault somewhere in the application path.

**Non-ephemeral and ephemeral services**

With the introduction of containers and microservices observability, monitoring solutions need to manage non-ephemeral and ephemeral services. We are collecting data for applications that consist of many different benefits.

So when it comes to container monitoring and performing chaos engineering kubernetes tests, we need to understand the nature and the full application that lies upon it. Everything is dynamic by nature. It would be best if you had monitoring and troubleshooting in place that can handle the dynamic and transient nature. When monitoring a containerized infrastructure, you should consider the following.

A: Container Lifespan: Containers have a short lifespan; they are provisioned and commissioned based on demand. This is compared to VMs or bare-metal workloads, which generally have a longer lifespan. As a generic guideline, containers have an average lifespan of 2.5 days, while traditional and cloud-based VMs have an average lifespan of 23 days. Containers can move, and they do move frequently.

One day, we could have workload A on cluster host A, and the next day or even on the same day, the same cluster host could be hosting Application workload B. Therefore, different types of impacts could depend on the time of day.

B: Containers are Temporary: Containers are dynamically provisioned for specific use cases temporarily. For example, we could have a new container based on a specific image. New network connections, storage, and any integrations to other services that make the application work will be set up for that container. All of this is done dynamically and can be done temporarily.

Example: Orchestration with Docker Swarm

C: Different monitoring levels: In a Kubernetes environment, there are many monitoring levels. The components that make up the Kubernetes deployment will affect application performance. We have nodes, pods, and application containers. We also monitor at different levels, such as the VM, storage, and microservice levels.

D: Microservices change fast and often: Microservices consist of constantly evolving apps. New microservices are added, and existing ones are decommissioned quickly. So, what does this mean to usage patterns? This will result in different usage patterns on the infrastructure. If everything is often changing, it can be hard to derive the baseline and build a topology map unless you have something automatic in place. 

E: Metric overload: We now have loads of metrics, including additional metrics for the different containers and infrastructure levels. We must consider metrics for the nodes, cluster components, cluster add-on, application runtime, and custom application metrics. This is compared to a traditional application stack, where we use metrics for components such as the operating system and the application. 

Metric Explosion

Note: Prometheus Scrapping Metrics

In the traditional world, we didn’t have to be concerned with the additional components such as an orchestrator or the dynamic nature of many containers. With a container cluster, we must consider metrics from the operating system, application, orchestrator, and containers.  We refer to this as a metric explosion. So now we have loads of metrics that need to be gathered. There are also different ways to pull or scrape these metrics.

Prometheus is expected in the world of Kubernetes and uses a very scalable pull approach to getting those metrics from HTTP endpoints either through Prometheus client libraries or exports.

Prometheus YAML file
Diagram: Prometheus YAML file

**A key point: What happens to visibility**  

So, we need complete visibility now more than ever—not just for single components but visibility at a holistic level. Therefore, we need to monitor a lot more data points than we had to in the past. We need to monitor the application servers, Pods and containers, clusters running the containers, the network for service/pod/cluster communication, and the host OS.

All of the monitoring data needs to be in a central place so trends can be seen and different queries to the data can be acted on. Correlating local logs would be challenging in a sizeable multi-tier application with docker containers. We can use Log forwarders or Log shippers, such as FluentD or Logstash, to transform and ship logs to a backend such as Elasticsearch.

**A key point: New avenues for monitoring**

Containers are the norm for managing workloads and adapting quickly to new markets. Therefore, new avenues have opened up for monitoring these environments. So we have, for example, AppDynamics and Elastic search, which are part of the ELK stack, and the various log shippers that can be used to help you provide a welcome layer of unification. We also have Prometheus to get metrics. Keep in mind that Prometheus works only in the land of metrics. There will be different ways to visualize all this data, such as Grafana and Kibana. 

Distributed Systems Visbility

What happens to visibility? We need complete visibility now more than ever, not just for single components but at a holistic level. Therefore, we need to monitor a lot more data points than we had to in the past. We need to monitor the application servers, Pods and containers, clusters running the containers, the network for service/pod/cluster communication, and the host OS. 

All of the monitoring data needs to be in a central place so trends can be seen and different queries to the data can be acted on. Correlating local logs would be challenging in a sizeable multi-tier application with docker containers. We can use Logforwarders or Log shippers, such as FluentD or Logstash, to transform and ship logs to a backend such as Elasticsearch.

Containers are the norm for managing workloads and adapting quickly to new markets. Therefore, new avenues have opened up for monitoring these environments. So, I have mentioned AppDynamics, Elastic search, which is part of the ELK stack, and the various log shippers that can be used to help you provide a layer of unification. We also have Prometheus. There will be different ways to visualize all this data, such as Grafana and Kibana. 

A: – Microservices Complexity

With the wave towards microservices, we get the benefits of scalability and business continuity, but managing them is very complex. The monolith is much easier to manage and monitor. Also, as separate components, they don’t need to be written in the same language or toolkits, so you can mix and match different technologies.

This approach has a lot of flexibility, but we can increase latency and complexity. There are a lot more moving parts that will increase complexity.

We have, for example, reverse proxies, load balancers, firewalls, and other infrastructure support services. What used to be method calls or interprocess calls within the monolith host now go over the network and are susceptible to deviations in latency. 

B: – Debugging Microservices

With the monolith, the application is simply running in a single process, and it is relatively easy to debug. Many traditional tooling and code instrumentation technologies have been built, assuming you have the idea of a single process. However, with microservices, we have a completely different approach with a distributed application.

Now, your application has multiple processes running in other places. The core challenge is trying to debug microservices applications.

So much of the tooling we have today has been built for traditional monolithic applications. So, there are new monitoring tools for these new applications, but there is a steep learning curve and a high barrier to entry. New tools and technologies such as distributed tracing and chaos engineering kubernetes are not the simplest to pick up on day one.

C: – Automation and monitoring

Automation comes into play with the new environment. With automation, we can do periodic checks not just on the functionality of the underlying components but also implement health checks of how the application performs. All can be automated for specific intervals or in reaction to certain events.

With the rise of complex systems and microservices, it is more important to have real-time performance monitoring and metrics that tell you how the systems behave. For example, what is the usual RTT, and how long can transactions occur under normal conditions?

Interactive Chaos Engineering / Observability Lab

Observability & Resilience

A chaos experiment is only useful if you can prove what happened, where it happened, how the application responded, and whether the system recovered.

Select Incident
Kubernetes Service Path
User Request
→
Ingress Entry point
→
Service Discovery
→
Pods Failure target
→
Node Compute
→
Cluster Control plane
Observability Signal Matrix

Metrics

p99 142ms
errors 0.3%

Quantifies system behaviour and SLO impact.

Logs

request OK
pod Ready

Shows application and component-level evidence.

Traces

124ms total
service OK

Shows where request time or failure occurred.

Events

Normal
Ready

Provides Kubernetes lifecycle evidence.

SLO

99.98%
Healthy

Connects telemetry to user-facing reliability.

Incident Timeline BASELINE
00:00
Baseline established — application operating normally.
00:05
Observability signals are within expected thresholds.
Engineering Takeaway

Never treat a single dashboard metric as proof of recovery. Correlate metrics, logs, traces and Kubernetes events, then verify that the user-facing SLO has returned to its expected state.

Docker security

Docker Container Security: Building a Sandbox

Docker Container Security

Docker containerization transformed application deployment by packaging software, libraries, configuration, and runtime dependencies into portable images. This improves consistency across development, testing, and production environments, but containerization does not eliminate the underlying security model of the host. Containers share the host operating system kernel, making container isolation, image integrity, runtime privileges, and host protection critical components of a secure deployment architecture.

Docker security therefore needs to be considered across the entire container lifecycle. A secure environment starts with the container image itself: using trusted base images, removing unnecessary packages and tools, keeping dependencies current, and continuously scanning images for known vulnerabilities. Reducing the image footprint also reduces the number of components that an attacker could potentially exploit.

Runtime configuration is equally important. Containers should operate with the least privilege required, rather than running as root by default. Capabilities, filesystem permissions, exposed ports, mounted host directories, Linux namespaces, and security profiles such as seccomp and AppArmor all influence the degree of isolation between a container and the underlying host. A compromised application should have as little access as possible to other workloads and host resources.

Security must also extend beyond individual containers. Authentication, authorization, secrets management, registry security, logging, and vulnerability monitoring form part of the wider container security model. Docker provides mechanisms such as image signing and trusted registries, while external security tooling can provide continuous assessment of images, dependencies, configurations, and runtime behaviour.

When containers are deployed through orchestration platforms such as Kubernetes, the security boundary becomes even broader. Cluster configuration, RBAC, service accounts, network policies, admission controls, secrets, pod security settings, and workload isolation all need to be considered. A vulnerable container running inside a well-controlled cluster presents a very different risk profile from one running with excessive privileges and unrestricted network access.

The objective of Docker container security is therefore not simply to scan an image and declare it safe. It is a defence-in-depth process spanning image creation, distribution, deployment, runtime execution, orchestration, monitoring, and incident response. By combining minimal images, least-privilege execution, trusted software sources, continuous vulnerability management, and strong runtime controls, organisations can take advantage of container portability without treating isolation as a substitute for security.

Interactive Docker Security / Sandbox Architecture

Docker Sandbox Architecture

Container security is not a single control. A hardened sandbox combines kernel isolation, resource controls, syscall filtering, mandatory access controls, identity and filesystem restrictions.

Security Layers
Layered Sandbox Model
Linux Namespaces PID • NET • MNT • IPC • UTS • USER
↓
Control Groups CPU • Memory • I/O • PIDs
↓
Seccomp System-call filtering
↓
AppArmor / SELinux Mandatory access control
↓
Rootless / Least Privilege Reduce privilege and breakout impact
↓
Network Isolation Control ingress, egress and lateral movement

Linux Namespaces

Namespaces create isolated views of system resources. They help prevent a containerized process from seeing the host's processes, interfaces, mounts and other resources.

PID namespace → isolated process tree
NET namespace → isolated interfaces
MNT namespace → isolated mount view
USER namespace → identity mapping
Engineering Takeaway

No single sandbox control should be treated as complete isolation. The security boundary becomes stronger when multiple independent controls reduce visibility, privilege, resource consumption, syscall access and network reachability.

Highlights: Docker Container Security

Understanding Docker Security

The Importance of Container Security

As the use of Docker containers grows, so does the potential attack surface. Containers are often deployed in large numbers, and any vulnerability can lead to significant breaches. Understanding the importance of container security is the first step in defending your applications. Ensuring that your containers are secure helps protect sensitive data, maintain application integrity, and uphold your organization’s reputation.

Docker provides numerous security features, such as isolated containers, resource limitations, and image signing. However, misconfigurations or overlooking certain best practices can leave Docker environments vulnerable to potential threats.Understanding the fundamentals of Docker security is essential to ensure a strong security foundation.

Containerization offers process-level isolation, which enhances security by preventing application conflicts and limiting the potential impact of vulnerabilities. Docker achieves this isolation through the use of namespaces and control groups.

Docker Container Security: Building a Sandbox Environment

Securing Docker containers requires more than scanning images or locking down registries — it demands sandboxing, isolating workloads so that even if a container is compromised, the attacker cannot escape, pivot, or damage the host. A Docker sandbox is a controlled execution environment with strict boundaries, hardened runtime controls, and minimal privileges. This section focuses on how to build a real sandbox, not just theoretical best practices.

Why Build a Sandbox for Docker?

Containers share the host kernel, which means a breakout can compromise the entire node. A sandbox reduces this risk by enforcing:

  • strict isolation
  • minimal privileges
  • controlled filesystem access
  • restricted networking
  • hardened runtime profiles
  • predictable behavior

A sandbox is essential for:

  • running untrusted workloads
  • multi‑tenant environments
  • CI pipelines executing user code
  • security testing and fuzzing
  • microservices exposed to the internet

Core Components of a Docker Sandbox

A secure sandbox combines multiple layers of isolation:

1. Namespaces — Process & Resource Isolation

Docker uses Linux namespaces to isolate:

  • processes
  • networking
  • mount points
  • IPC
  • UTS (hostname)
  • PID trees

A sandbox ensures these namespaces are not shared with the host or other containers.

2. Control Groups (cgroups) — Resource Limits

Sandboxing requires strict limits on:

  • CPU
  • memory
  • I/O
  • PIDs
  • network bandwidth

This prevents runaway containers from exhausting host resources.

3. Seccomp — System Call Filtering

Seccomp profiles restrict which syscalls a container can execute.

A sandbox uses a deny‑by‑default profile, blocking dangerous syscalls such as:

  • ptrace
  • mount
  • clone with privileged flags
  • setns
  • reboot

This is one of the strongest isolation layers.

4. AppArmor / SELinux — Mandatory Access Control

MAC frameworks enforce filesystem and process restrictions.

Sandbox profiles prevent containers from:

  • writing to host files
  • accessing sensitive directories
  • modifying kernel parameters
  • escalating privileges

5. Rootless Mode — No Host Root Access

Running Docker in rootless mode ensures:

  • container root ≠ host root
  • reduced breakout impact
  • limited kernel attack surface

This is essential for multi‑tenant environments.

Building a Docker Sandbox: Practical Steps

Here is a practical, real‑world sandbox design.

Step 1 — Use a Minimal Base Image

Start with:

  • alpine
  • distroless
  • scratch

Fewer binaries = fewer attack vectors.

Step 2 — Run Containers Without Root

Use: USER 1000

Or run Docker itself in rootless mode.

Step 3 — Apply a Custom Seccomp Profile

Example (conceptual):

{
“defaultAction”: “SCMP_ACT_ERRNO”,
“syscalls”: [
{ “names”: [“read”, “write”, “exit”], “action”: “SCMP_ACT_ALLOW” }
]}

Allow only essential syscalls.

Step 4 — Apply AppArmor or SELinux Policies

Example AppArmor profile:

profile docker-sandbox flags=(attach_disconnected) {
file,
deny /proc/** w,
deny /sys/** w,
}

Restrict host filesystem access.

Step 5 — Restrict Networking

Use:

  • isolated Docker networks
  • no host networking
  • egress filtering
  • DNS restrictions

Sandbox containers should not reach internal services unless explicitly allowed.

Step 6 — Limit Resources

Example:docker run –cpus=0.5 –memory=256m –pids-limit=100 …

Prevents resource exhaustion attacks.

Step 7 — Use Read‑Only Filesystems

Example:docker run –read-only -v /tmp …

Prevents tampering with container files.

Advanced Sandbox Techniques

For high‑security environments:

gVisor

A user‑space kernel that intercepts syscalls.

Excellent for running untrusted code.

Kata Containers

Lightweight VMs that isolate containers with hardware virtualization.

Firecracker

Micro‑VMs used by AWS Lambda for strong isolation.

User Namespaces

Map container root to an unprivileged host user.

These tools provide stronger isolation than Docker alone.

Monitoring and Observability Inside the Sandbox

A sandbox must include visibility:

  • syscall auditing
  • container logs
  • network flow monitoring
  • anomaly detection
  • file integrity monitoring

Tools like Falco, Sysdig, and OpenTelemetry help detect breakout attempts.

Outcome — A Hardened, Secure Container Execution Environment

A Docker sandbox ensures:

  • untrusted workloads cannot escape
  • compromised containers cannot pivot
  • resource exhaustion attacks are contained
  • filesystem tampering is blocked
  • dangerous syscalls are denied
  • identity‑aware policies enforce Zero Trust

This transforms Docker from “lightweight virtualization” into a secure, controlled execution environment suitable for modern cloud‑native workloads.

**Docker Container Security: Best Practices and Strategies**

### Understanding Docker and Its Popularity

Docker has revolutionized the way we think about software deployment and scalability. By packaging applications and their dependencies into containers, developers can ensure consistency across various environments. This portability is a primary reason for Docker’s widespread adoption in the tech industry. However, with great power comes great responsibility, and securing these containers is crucial for maintaining the integrity and reliability of applications.

### The Importance of Container Security

As the use of Docker containers grows, so does the potential attack surface. Containers are often deployed in large numbers, and any vulnerability can lead to significant breaches. Understanding the importance of container security is the first step in defending your applications. Ensuring that your containers are secure helps protect sensitive data, maintain application integrity, and uphold your organization’s reputation.

### Best Practices for Securing Docker Containers

1. **Use Official and Trusted Images**: Always start with images from trusted sources, such as Docker Hub’s official repository. These images are maintained and updated regularly, reducing the risk of vulnerabilities.

2. **Regularly Update and Patch**: Just as with any software, keeping your container images up to date is crucial. Regular updates and patches can fix known vulnerabilities and improve overall security.

3. **Limit Container Privileges**: Containers should run with the least privileges necessary. Avoid running containers as the root user and apply the principle of least privilege to minimize potential damage from an attack.

4. **Implement Network Segmentation**: Isolate your containers by using Docker’s network features. This segmentation limits communication to only what is necessary, reducing the risk of lateral movement by attackers.

5. **Monitor and Log Activity**: Continuous monitoring and logging of container activity can help detect unusual patterns or potential breaches. Tools like Docker Security Scanning and third-party solutions can provide insights into container behavior.

Example: Container segmentation

Containers can be part of two networks. Consider the bridge network a standard switch, except it is virtual. Anything attached can communicate. So, if we have a container with two virtual ethernet cards connected to two different switches, the container is in two networks.

inspecting container networks
Diagram: Inspecting container networks

Personal Note: Docker Security Vendors

In addition to docker security best practices that we will dicuss soon, leveraging modern tools and technologies can significantly enhance Docker container security. Security solutions such as Aqua Security, Sysdig Secure, and Twistlock offer advanced features like vulnerability scanning, runtime defense, and compliance checks. Incorporating these tools into your security strategy can provide an added layer of protection against sophisticated threats.

Docker Security Key Points

-Building your Docker images on a solid foundation is crucial for container security. Employing secure image practices includes regularly updating base images, minimizing the software installed within the container, and scanning images for vulnerabilities using tools like Docker Security Scanning.

-Controlling access to Docker containers is vital in maintaining their security. This involves utilizing user namespaces, restricting container capabilities, and employing role-based access control (RBAC) to limit privileges and prevent unauthorized access.

-Keeping a close eye on container activities is essential for identifying potential security breaches. Implementing centralized logging and monitoring solutions helps track and analyze container events, enabling timely detection and response to any suspicious or malicious activities.

Docker offers numerous security features, such as isolation, resource management, and process restrictions. However, these features alone may not provide sufficient protection against sophisticated attacks. That’s where SELinux steps in.

A. SELinux: Security Framework

SELinux is a security framework integrated into the Linux kernel. It provides an additional layer of access control policies, enforcing mandatory access controls (MAC) beyond the traditional discretionary access controls (DAC). By leveraging SELinux, administrators can define fine-grained policies that govern processes’ behavior and limit their access to critical resources.

Docker has built-in support for SELinux, allowing users to enable and enforce SELinux policies on containers and their underlying hosts. By enforcing SELinux policies, containers are further isolated from the host system, reducing the potential impact of a compromised container on the overall infrastructure.

B. Namespaces and Control Groups

The building blocks for Docker security options and implementing Docker security best practices, such as the Kernel primitives, have been around for a long time, so they are not all new when considering their security. However, the container itself is not a kernel construct; it is an abstraction of using features of the host operating system kernel. For Docker container security and building a Docker sandbox, these kernel primitives are the namespaces and control groups that allow the abstraction of the container.

-Namespaces provide processes with isolated instances of various system resources. They allow for resource virtualization, ensuring that processes within one namespace cannot interfere with processes in another. Namespaces provide separation at different levels, such as mount points, network interfaces, process IDs, and more. By doing so, they enable enhanced resource utilization and security.

-Control groups, called cgroups, allow for resource limitation, prioritization, and monitoring. They allow administrators to allocate resources, such as CPU, memory, disk I/O, and network bandwidth, to specific groups of processes. Control groups ensure fair distribution of resources among different applications or users, preventing resource hogging and improving overall system performance.

Namespaces and control groups offer a wide range of applications and benefits. In containerization technologies like Docker, namespaces provide the foundation for creating isolated environments, allowing multiple containers to run on a single host without interference. Control groups, on the other hand, enable fine-grained resource control and optimization within these containers, ensuring fair resource allocation and preventing resource contention.

C. The Role of Kernel Primitives

To build a docker sandbox, Docker uses control groups to control workloads’ resources to host resources. As a result, Docker allows you to implement system controls with these container workloads quickly. Fortunately, much of the control group complexity is hidden behind the Docker API, making containers and Container Networking much easier to use.

Then, we have namespaces that control what a container can see. A namespace allows us to take an O/S with all its resources, such as filesystems, and carve it into virtual operating systems called containers. Namespaces are like visual boundaries, and there are several different namespaces.

Example Techniques: Docker Bench Security

Docker Bench is an open-source tool developed by Docker, Inc. It aims to provide a standardized way of assessing Docker security configurations. By running Docker Bench, we can identify security weaknesses, misconfigurations, and potential vulnerabilities in our Docker setup. The tool provides a comprehensive checklist based on industry best practices, including security recommendations from Docker themselves.

To enhance Docker security using Docker Bench, we must follow a few simple steps. First, we need to install Docker Bench on our host machine. Once installed, we can execute the tool to assess our Docker environment. Docker Bench will evaluate various aspects, such as Docker daemon configuration, host security settings, container configurations, and more. It will then provide a detailed report highlighting areas requiring attention.

Privilege Escalation: The Importance of a Sandbox

During an attack, your initial entry point into a Linux system is via a low-privileged account, which provides you with a low-privileged shell. To obtain root-level access, you need to escalate your privileges. This is generally done by starting with enumeration.

Sometimes, your target machine may have misconfigurations that you could leverage to escalate your privileges. Here, we will look for misconfigurations, particularly those that leverage the SUID (Set User Identification) permission.

The SUID permission allows low-privileged users to run an executable with the file system permissions of its owner. For example, if the system installs an executable globally, that executable would run as root. 

Note: A quick way to find all executables with SUID permission is to execute the command labeled Number 1 in the screenshot below. I’m just running Ubuntu on a VM. To illustrate how the SUID permission can be abused for privilege escalation, you will use the found executable labeled number 2.

Privilege Escalation

Analysis:

    • After setting the SUID permission, re-run the command to find all executables. You will see /usr/bin/find now appears in the list.
    • Since find has the SUID permission, you can leverage it to execute commands in the root context. 
    • You should now see the contents of the /etc/shadow file. This file is not visible without root permissions. You can leverage additional commands to execute more tasks and gain a high-privilege backdoor from here.

privilege attacks

The following example shows running a container with an unprivileged user, not a root user. Therefore, we are not roots inside the container. With this example, we are using user ID 1500. Notice how we can’t access the /etc/shadow password file, which is a file that needs root privileges to open. To mitigate the risks associated with running containers as root, organizations should adopt the following best practices:

container security

Proactive Measures to Mitigate Privilege Escalation:

To safeguard against privilege escalation attacks, individuals and organizations should consider implementing the following measures:

1. Regular Software Updates:

Keeping operating systems, applications, and software up to date with the latest security patches helps mitigate vulnerabilities that can be exploited for privilege escalation.

2. Strong Access Controls:

Implementing robust access control mechanisms, such as the principle of least privilege, helps limit user privileges to the minimum level necessary for their tasks, reducing the potential impact of privilege escalation attacks.

3. Multi-factor Authentication:

Enforcing multi-factor authentication adds an extra layer of security, making it more difficult for attackers to gain unauthorized access even if they possess stolen credentials.

4. Security Audits and Penetration Testing:

Regular security audits and penetration testing can identify vulnerabilities and potential privilege escalation paths, allowing proactive remediation before attackers can exploit them.

**Containerized Processes**

Containers are often referred to as “containerized processes.” Essentially, a container is a Linux process running on a host machine. However, the process has a limited view of the host and can access a subtree of the filesystem. Therefore, it would be best to consider a container a process with a restricted view.

Namespace and resource restrictions provide the limited view offered by control groups. The inside of the container looks similar to that of a V.M. with isolated processes, networking, and file system access. However, it seems like a normal process running on the host machine from the outside. 

**Container Security** 

One of the leading security flaws to point out when building a docker sandbox is that containers, by default, run as root.  Notice in the example below that we have a tool run on the Docker host that can perform an initial security scan – called Docker Bench. Remember that running containers as root comes with inherent security risks that organizations must consider carefully. Here are some key concerns:

Note:

1. Exploitation of Vulnerabilities: Running containers as root increases the potential impact of vulnerabilities within the container. If an attacker gains access to a container running as root, they can potentially escalate their privileges and compromise the host system.

2. Escaping Container Isolation: Containers rely on a combination of kernel namespaces, cgroups, and other isolation mechanisms to provide separation from the host and other containers. Running containers as root increases the risk of an attacker exploiting a vulnerability within the container to escape this isolation and gain unauthorized access to the host system.

3. Unauthorized System Access: If a container running as root is compromised, the attacker may gain full access to the underlying host system, potentially leading to unauthorized system modifications, data breaches, or even the compromise of other containers running on the same host.

Containers running as root

Ensuring Kubernetes Security

As with any technology, security is paramount when it comes to Kubernetes. Securing Kubernetes clusters involves multiple layers, starting with the control plane. It’s crucial to limit access to the Kubernetes API server by implementing authentication and authorization mechanisms. Role-Based Access Control (RBAC) is a vital feature that helps define permissions for users and applications, ensuring that only authorized entities can perform specific actions.

Network policies also play a critical role in securing Kubernetes environments. By restricting communication between pods and services, network policies help mitigate the risk of unauthorized access. Additionally, regular audits and monitoring are essential to detect and respond to potential threats in real-time.

Kubernetes network policy

**Best Practices for Managing Kubernetes Clusters**

Effective management of Kubernetes clusters requires adherence to certain best practices. First and foremost, it’s important to keep all Kubernetes components up to date. Regular updates not only provide new features but also address security vulnerabilities.

Another best practice is to implement resource requests and limits for containers. By defining these parameters, you can ensure that applications have the necessary resources to function optimally while preventing any single application from monopolizing the cluster’s resources.

Lastly, consider using namespaces to organize resources within a cluster. Namespaces provide a way to divide cluster resources between multiple users or teams, improving resource allocation and management.

 

Interactive Docker Security / Hardening Lab

Container Security Hardening Lab

Start with a deliberately weak container configuration. Apply security controls one layer at a time and watch the exposed attack surface shrink.

Apply Security Controls
Run as non-root Use an unprivileged container user
Read-only filesystem Reduce filesystem modification
Drop capabilities Remove unnecessary Linux privileges
Restrict syscalls Apply a tighter seccomp policy
Limit resources Constrain CPU, memory and PIDs
Restrict networking Reduce unnecessary connectivity
Security posture 20 / 100
High exposure — multiple unnecessary privileges remain.
Container Security Console
security-console — container/web-app
$ docker inspect web-app
Security assessment started...
User: root
Filesystem: writable
Capabilities: default set
Seccomp: default profile
Resource limits: not defined
Network: unrestricted
docker run web-app
Privilege Container starts with root identity.
Filesystem Writable filesystem increases tampering risk.
Runtime Additional runtime restrictions are required.
Engineering Takeaway

Container hardening is cumulative. No individual control provides complete protection; the objective is to reduce privilege, visibility, resource abuse and available attack paths together.

Docker Container Security

Understanding Containers

For a long time, big web-scale players have been operating container technologies to manage the weaknesses of the VM model. In the container model, the container is analogous to the VM. However, a significant difference is that containers do not require their full-blown OS. Instead, all containers operating on a single host share the host’s OS.

This frees up many system resources, such as CPU, RAM, and storage. Containers are again fast to start and ultra-portable. Consequently, moving containers with their application workloads from your laptop to the cloud and then to VMs or bare metal in your data center is a breeze.

Docker Container Diagram
Diagram: Docker Container. Source Docker.

Sandbox containers

– Sandbox containers are a virtualization technology that provides a secure environment for applications and services to run in. They are lightweight, isolated environments that run applications and services safely without impacting the underlying host.

– This virtualization technology enables rapid application deployment while providing a secure environment to isolate, monitor, and control access to data and resources. Sandbox containers are becoming increasingly popular as they offer an easy, cost-effective way to securely deploy and manage applications and services.

– Sandbox containers can also be used for testing, providing a safe and isolated environment for running experiments. In addition, they are highly scalable and can quickly and easily deploy applications across multiple machines. This makes them ideal for large-scale projects, as they can quickly deploy and manage applications on a large scale. The following figures provide information that is generic to sandbox containers.

Benefits of Using a Docker Sandbox:

1. Replicating Production Environment: A Docker sandbox allows developers to create a replica of the production environment. This ensures the application runs consistently across different environments, reducing the chances of unexpected issues when the code is deployed.

2. Isolated Development Environment: With Docker, developers can create a self-contained environment with all the necessary dependencies. This eliminates the hassle of manually setting up development environments and ensures team consistency.

3. Fast and Easy Testing: Docker sandboxes simplify testing applications in different scenarios. By creating multiple sandboxes, developers can test various configurations, such as different operating systems, libraries, or databases, without interfering with each other.

Docker Sandbox
Diagram: Docker Sandbox.

Understanding the Risks: Docker containers present unique security challenges that must be addressed. We will delve into the potential risks associated with containerization, including container breakouts, image vulnerabilities, and compromised host systems.

Implementing Container Isolation: One fundamental aspect of securing Docker containers is isolating them from each other and the host system. We will explore techniques such as namespace and cgroup isolation and use security profiles to strengthen container isolation and prevent unauthorized access.

Regular Image Updates and Vulnerability Scanning: Keeping your Docker images up to date is vital for maintaining a secure container environment. We will discuss the importance of regularly updating base images and utilizing vulnerability scanning tools to identify and patch potential security vulnerabilities in your container images.

Container Runtime Security: The container runtime environment plays a significant role in container security. We will explore runtime security measures such as seccomp profiles, AppArmor, and SELinux to enforce fine-grained access controls and reduce the attack surface of your Docker containers.

Monitoring and Auditing Container Activities

Effective monitoring and auditing mechanisms are essential for promptly detecting and responding to security incidents. We will explore tools and techniques for monitoring container activities, logging container events, and implementing intrusion detection systems specific to Docker containers.

container attack vectors
Diagram: Container attack vectors. Source Adriancitu

**Step-by-Step Guide to Building a Docker Sandbox**

Step 1: Install Docker:

The first step is to install Docker on your machine. Docker provides installation packages for various operating systems, including Windows, macOS, and Linux. Visit the official Docker website and follow the installation instructions specific to your operating system.

Step 2: Set Up Dockerfile:

A Dockerfile is a text file that contains instructions for building a Docker image. Create a new ” Dockerfile ” file and define the base image, dependencies, and any necessary configuration. This file serves as the blueprint for your Docker sandbox.

Step 3: Build the Docker Image:

Once the Dockerfile is ready, you can build the Docker image by running the “docker build” command. This command reads the instructions from the Dockerfile and creates a reusable image that can be used to run containers.

Step 4: Create a Docker Container:

Once you have built the Docker image, you can create a container based on it. Containers are instances of Docker images that can be started, stopped, and managed. Use the “docker run” command to create a container from the image you built.

Step 5: Configure the Sandbox:

Customize the Docker container to match your requirements. This may include installing additional software, setting environment variables, or exposing ports for communication. Use the Docker container’s terminal to make these configurations.

Step 6: Test and Iterate:

Once the sandbox is configured, you can test your application within the Docker environment. Use the container’s terminal to execute commands, run tests, and verify that your application behaves as expected. If necessary, make further adjustments to the container’s configuration and iterate until you achieve the desired results.

**Docker Container Security: Building a Docker Sandbox**

So, we have some foundational docker container security that has been here for some time. A Linux side of security will give us things such as namespace, control groups we have just mentioned, secure computing (seccomp), AppArmor, and SELinux that provide isolation and resource protection. Consider these security technologies to be the first layer of security that is closer to the workload. Then, we can expand from there and create additional layers of protection, creating an in-depth defense strategy.

Container Sandbox
Diagram: Building a Sandbox. Source Aqua

How to create a Docker sandbox environment

As a first layer to creating a Docker sandbox, you must consider the available security module templates. Several security modules can be implemented that can help you enable fine-grained access control or system resources hardening your containerized environment. More than likely, your distribution comes with a security model template for Docker containers, and you can use these out of the box for some use cases.

However, you may need to tailor the out-of-the-box default templates for other use cases. Templates for Secure Computing, AppArmor, and SELinux will be available. These templates, the Dockerfile, and workload best practices will give you an extra safety net. 

Docker container security and protection

Containers run as root by default.

The first thing to consider when starting Docker container security is that containers run as root by default and share the Kernel of the Host OS. They rely on the boundaries created by namespaces for isolation and control groups to prevent one container from consuming resources negatively. So here, we can avoid things like a noisy neighbor, where one application uses up all resources on the system, preventing other applications from performing adequately on the same system.

In the early days of containers, this is how container protection started with namespace and control groups, and the protection was not perfect. For example, it cannot prevent all interference in resources the operating system kernel does not manage. 

So, we need to move to a higher abstraction layer with container images. The container images encapsulate your application code and any dependencies, third-party packages, and libraries. Images are our built assets representing all the fields to run our application on top of the Linux kernel. In addition, images are used to create containers so that we can provide additional Docker container security here.

Docker Container Security
Diagram: Docker Container Security. Rootless mode. Source Aquasec.

Security concerns. Image and supply chain

To run a container, we need to pull images. The images are pulled locally or from remote registries; we can have vulnerabilities here. Your hosts connected to the registry may be secure, but that does not mean the image you are pulling is secure. Traditional security appliances are blind to malware and other image vulnerabilities as they are looking for different signatures. There are several security concerns here.

Users can pull full or bloated images from untrusted registries or images containing malware. As a result, we need to consider the container threats in both runtimes and the supply chain for adequate container security.

Scanning Docker images during the CI stage provides a quick and short feedback loop on security as images are built. You want to discover unsecured images well before you deploy them and enable developers to fix them quickly rather than wait until issues are found in production.

You should also avoid unsecured images in your testing/staging environments, as they could also be targets for attack. For example, we have image scanning from Aqua, and image assurance can be implemented in several CI/CD tools, including the Codefresh CI/CD platform.

Container image security
Diagram: Container image security. Source Aqua.

Note:

1. Principle of Least Privilege: Avoid running containers as root whenever possible. Instead, use non-root users within the container and assign only the necessary privileges required for the application to function correctly.

2. User Namespace Mapping: Utilize user namespace mapping to map non-root users inside the container to a different user outside the container. This helps provide an additional layer of isolation and restricts the impact of any potential compromise.

3. Secure Image Sources: Ensure container images from trusted sources are obtained in your environment. Regularly update and patch container images to minimize the risk of known vulnerabilities.

4. Container Runtime Security: Implement runtime security measures such as container runtime security policies, secure configuration practices, and regular vulnerability scanning to detect and prevent potential security breaches.

Security concerns: Container breakouts

The container process is visible from the host. Therefore, if a bad actor gets access to the host with the correct privileges, it can compromise all the containers on the host. If an application can read the memory that belongs to your application, it can access your data. So, you need to ensure that your applications are safely isolated from each other. If your application runs on separate physical machines, accessing another application’s memory is impossible. From the security perspective, physical isolation is the strongest but is often not always possible. 

If a host gets compromised, all containers running on the host are potentially compromised, too, especially if the attacker gains root or elevates their privileges, such as a member of the Docker Group.

Your host must be locked down and secured, making container breakouts difficult. Also, remember that it’s hard to orchestrate a container breakout. Still, it is not hard to misconfigure a container with additional or excessive privileges that make a container breakout easy.

Docker Container Security
Diagram: Docker container security. Source Aqua.

The role of the Kernel: Potential attack vector

The Kernel manages its userspace processes and assigns memory to each process. So, it’s up to the Kernel to ensure that one application can’t access the memory allocated to another. The Kernel is hardened and battle-tested, but it is complex, and complexity is the number one enemy of good security. You cannot rule out a bug in how the Kernel manages memory; an attacker could exploit that bug to access the memory of other applications.

**Hypervisor: Better isolation? Kernel attack surface**

So, does the Hypervisor give you better isolation than a Kernel gives to its process? The critical point is that a kernel is complex and constantly evolving; as crucial as it manages memory and device access, the Hypervisor has a more specific role. As a result, the hypervisors are smaller and more straightforward than whole Linux kernels. 

What happens if you compare the lines of code in the Linux Kernel to that of an open-source hypervisor? Less code means less complexity, resulting in a smaller attack surface—a more minor attack surface increases the likelihood of a bad actor finding an exploitable flaw.

With a kernel, the userspace process allows some visibility of each other. For example, you can run specific CLI commands and see the running processes on the same machine. Furthermore, you can access information about those processes with the correct permissions. 

This fundamentally differs between the container and V.M. Many consider the container weaker in isolation. With a V.M., you can’t see one machine’s process from another. The fact that containers share a kernel means they have weaker isolation than the V.M. For this reason and from the security perspective, you can place containers into V.Ms.

Docker Security Best Practices – Goal1: Strengthen isolation: Namespaces

One of the main building blocks of containers is a Linux construct called the namespace, which provides a security layer for your applications running inside containers. For example, you can limit what that process can see by putting a process in a namespace. A namespace fools a process that it uniquely has access to. In reality, other processes in their namespace can access similar resources in their isolated environments. The resources belong to the host system.

Docker Security Best Practices – Goal2: Strengthen isolation: Access control

Access control is about managing who can access what on a system. We inherited Unix’s Discretionary Access Control (DAC) features from Linux. Unfortunately, they are constrained, and there are only a few ways to control access to objects. If you want a more fine-grained approach, we have Mandatory Access Control (MAC), which is policy-driven and granular for many object types.

We have a few solutions for MAC. For example, SELinux was in Kernel in 2003 and AppArmor in 2010. These are the most popular in the Linux domain, and these are implemented as modules via the LSM framework.

SELinux was created by the National Security Agency (NSA ) to protect systems and was integrated into the Linux Kernel. It is a Linux kernel security module that has access controls, integrity controls, and role-based access controls (RBAC)

Docker Security Best Practices – Goal3: Strengthen isolation: AppArmor

AppArmor applies access control on an application-by-application basis. To use it, you associate an AppArmor security profile with each program. Docker loads a default profile for the container’s default. Keep in mind that this is used and not on the Docker Daemon. The “default profile” is called docker-default. Docker describes it as moderately protective while providing broad application capability.

So, instantiating a container uses the “docker default” policy unless you override it with the “security-opt” flag. This policy is crafted for the general use case. The default profile is applied to all container workloads if the host has AppArmor enabled. 

Docker Security Best Practices – Goal4: Strengthen isolation: Control groups

Containers should not starve other containers from using all the memory or other host resources. So, we can use control groups to limit resources available to different Linux processes. Control Groups control hosts’ resources and are essential for fending Denial-of-Service Attacks. If a function is allowed to consume, for example, unlimited memory, it can starve other processes on the same host of that host resource.

This could be done inadvertently through a memory leak in the application or maliciously due to a resource exhaustion attack that takes advantage of a memory leak. The container can fork as many processes (PID ) as the max configured for the host kernel.

Unchecked, this is a significant avenue as a DoS. A container should be limited to the number of processors required through the CLI. A control group called PID determines the number of processes allowed within a control group to prevent a fork bomb attack. This can be done with the PID subsystem.

Docker Security Best Practices – Goal5: Strengthen isolation: Highlighting system calls 

System calls run in the Kernel space, with the highest privilege level and kernel and device drivers. At the same time, a user application runs in the user space, which has fewer privileges.  When an application that runs in user space needs to carry out such tasks as cloning a process, it does this via the Kernel, and the Kernel carries out the operation on behalf of the userspace process. This represents an attack surface for a bad actor to play with.

Docker Security Best Practices – Goal6: Security standpoint: Limit the system calls

So, you want to limit the system calls available to an application. If a process is compromised, it may invoke system calls it may not ordinarily use. This could potentially lead to further compromisation.  It would help if you aimed to remove system calls that are not required and reduce the available attack surface. As a result, it will reduce the risk of compromise and risk to the containerized workloads.

Docker Security Best Practices – Goal7: Secure Computing Mode

Secure Computing Mode (seccomp) is a Linux kernel feature that restricts the actions available within the containers. For example, there are over 300+ syscalls in the Linux system call interface, and your container is unlikely to need access. For instance, if you don’t want containers to change kernel modules. Therefore, they do not need to call the “create” module, “delete” module, or “init”_module.” Seccomp profiles are applied to a process that determines whether or not a given system call is permitted. Here, we can list or blocklist a set of system calls.

The default seccomp profile sets the Kernel’s action when a container process attempts to execute a system call. An allowed action specifies an unconditional list of permitted system calls.

For Docker container security, the default seccomp profile blocks over 40 syscalls without ill effects on the containerized applications. You may want to tailor this to suit your security needs, restrict it further, and limit your container to a smaller group of syscalls. It is recommended that each application have a seccomp profile that permits precisely the same syscalls it needs to function. This will follow the security principle of the least privileged.

Docker Container Security • Block 3

Runtime & Supply Chain Defense

Container security does not end when an image is built. A hardened workload also needs trusted images, runtime monitoring, anomaly detection and controls that reduce the impact of a compromised container.

Select a defense layer
Defense layer

Image Security

Start with a trusted, minimal image and continuously scan it for known vulnerabilities before it reaches production.

Primary goal Reduce vulnerable software and unnecessary packages.
Security signal Image scan / SBOM / signed artifact
Risk reduced Known vulnerable components
Engineering principle Build only what the workload needs.
Defensive example
# Scan the image before deployment
docker scout cves myapp:1.0

# Keep the runtime image minimal
FROM alpine:3.20
Container security posture 25%
Baseline established. Additional runtime controls are recommended.