Cisco ACI

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Cisco ACI Components

In today's rapidly evolving technological landscape, organizations are constantly seeking innovative solutions to streamline their network infrastructure. Enter Cisco ACI Networks, a game-changing technology that promises to redefine networking as we know it. In this blog post, we will explore the key features and benefits of Cisco ACI Networks, shedding light on how it is transforming the way businesses design, deploy, and manage their network infrastructure.

Cisco ACI, short for Application Centric Infrastructure, is an advanced networking solution that brings together physical and virtual environments under a single, unified policy framework. By providing a holistic approach to network provisioning, automation, and orchestration, Cisco ACI Networks enable organizations to achieve unprecedented levels of agility, efficiency, and scalability.

Simplified Network Management: Cisco ACI Networks simplify network management by abstracting the underlying complexity of the infrastructure. With a centralized policy model, administrators can define and enforce network policies consistently across the entire network fabric, regardless of the underlying hardware or hypervisor.

Enhanced Security: Security is a top concern for any organization, and Cisco ACI Networks address this challenge head-on. By leveraging microsegmentation and integration with leading security platforms, ACI Networks provide granular control and visibility into network traffic, helping organizations mitigate potential threats and adhere to compliance requirements.

Scalability and Flexibility: The dynamic nature of modern business demands a network infrastructure that can scale effortlessly and adapt to changing requirements. Cisco ACI Networks offer unparalleled scalability and flexibility, allowing businesses to seamlessly expand their network footprint, add new services, and deploy applications with ease.

Data Center Virtualization: Cisco ACI Networks have revolutionized data center virtualization by providing a unified fabric that spans physical and virtual environments. This enables organizations to achieve greater operational efficiency, optimize resource utilization, and simplify the deployment of virtualized workloads.

Multi-Cloud Connectivity: In the era of hybrid and multi-cloud environments, connecting and managing disparate cloud services can be a daunting task. Cisco ACI Networks facilitate seamless connectivity between on-premises data centers and various public and private clouds, ensuring consistent network policies and secure communication across the entire infrastructure.

Cisco ACI Networks offer a paradigm shift in network infrastructure, empowering organizations to build agile, secure, and scalable networks tailored to their specific needs. With its comprehensive feature set, simplified management, and seamless integration with virtual and cloud environments, Cisco ACI Networks are poised to shape the future of networking. Embrace this transformative technology, and unlock a world of possibilities for your organization.

Matt Conran

Highlights: Cisco ACI Components

The ACI Fabric

Cisco ACI is a software-defined networking (SDN) solution that integrates with software and hardware. With the ACI, we can create software policies and use hardware for forwarding, an efficient and highly scalable approach offering better performance. The hardware for ACI is based on the Cisco Nexus 9000 platform product line. The APIC centralized policy controller drives the software, which stores all configuration and statistical data.

–The Cisco Nexus Family–

To build the ACI underlay, you must exclusively use the Nexus 9000 family of switches. You can choose from modular Nexus 9500 switches or fixed 1U to 2U Nexus 9300 models. Specific models and line cards are dedicated to the spine function in ACI fabric; others can be used as leaves, and some can be used for both purposes. You can combine various leaf switches inside one fabric without any limitations.

a) Cisco ACI Fabric: Cisco ACI’s foundation lies in its fabric, which forms the backbone of the entire infrastructure. The ACI fabric comprises leaf switches, spine switches, and the application policy infrastructure controller (APIC). Each component ensures a scalable, agile, and resilient network.

b) Leaf Switches: Leaf switches serve as the access points for endpoints within the ACI fabric. They provide connectivity to servers, storage devices, and other network devices. With their high port density and advanced features, such as virtual port channels (vPCs) and fabric extenders (FEX), leaf switches enable efficient and flexible network designs.

c) Spine Switches: Spine switches serve as the core of the ACI fabric, providing high-bandwidth connectivity between the leaf switches. They use a non-blocking, multipath forwarding mechanism to ensure optimal traffic flow and eliminate bottlenecks. With their modular design and support for advanced protocols like Ethernet VPN (EVPN), spine switches offer scalability and resiliency.

d) Application Policy Infrastructure Controller (APIC): At the heart of Cisco ACI is the APIC, a centralized management and policy control plane. The APIC acts as a single control point, simplifying network operations and enabling policy-based automation. It provides a comprehensive view of the entire fabric, allowing administrators to define and enforce policies across the network.

e) Integration with Virtualization and Cloud Environments: Cisco ACI seamlessly integrates with virtualization platforms such as VMware vSphere and Microsoft Hyper-V and cloud environments like Amazon Web Services (AWS) and Microsoft Azure. This integration enables consistent policy enforcement and visibility across physical, virtual, and cloud infrastructures, enhancing agility and simplifying operations.

–ACI Architecture: Spine and Leaf–

To be used as ACI spines or leaves, Nexus 9000 switches must be equipped with powerful Cisco CloudScale ASICs manufactured using 16-nm technology. The following figure shows the Cisco ACI based on the Nexus 9000 series. Cisco Nexus 9300 and 9500 platform switches support Cisco ACI. As a result, organizations can use them as spines or leaves to utilize an automated, policy-based systems management approach fully. 

Cisco ACI Components
Diagram: Cisco ACI Components. Source is Cisco

**Hardware-based Underlay**

Server virtualization helped by decoupling workloads from the hardware, making the compute platform more scalable and agile. However, the server is not the main interconnection point for network traffic. So, we need to look at how we could virtualize the network infrastructure similarly to the agility gained from server virtualization.

**Mapping Network Endpoints**

This is carried out with software-defined networking and overlays that could map network endpoints and be spun up and down as needed without human intervention. In addition, the SDN architecture includes an SDN controller and an SDN network that enables an entirely new data center topology.

**Specialized Forwarding Chips**

In ACI, hardware-based underlay switching offers a significant advantage over software-only solutions due to specialized forwarding chips. Furthermore, thanks to Cisco’s ASIC development, ACI brings many advanced features, including security policy enforcement, microsegmentation, dynamic policy-based redirect (inserting external L4-L7 service devices into the data path), or detailed flow analytics—besides the vast performance and flexibility.

Related: For pre-information, you may find the following helpful:

  1. Data Center Security 
  2. VMware NSX

Cisco ACI Components

 Introduction to Leaf and Spine

The Cisco SDN ACI works with a Clos architecture, a fully meshed ACI network. Based on a spine leaf architecture. As a result, every Leaf is physically connected to every Spine, enabling traffic forwarding through non-blocking links. Physically, a leaf switch set creates a leaf layer attached to the spines in a full BIPARTITE graph. This means that each Leaf is connected to each Spine, and each Spine is connected to each Leaf. 

The ACI uses a horizontally elongated Leaf and Spine architecture with one hop to every host in an entirely messed ACI fabric, offering good throughput and convergence needed for today’s applications.

The ACI fabric: Does Not Aggregate Traffic

A key point in the spine-and-leaf design is the fabric concept, like a stretch network. One of the core ideas around a fabric is that it does not aggregate traffic. This does increase data center performance along with a non-blocking architecture. With the spine-leaf topology, we are spreading a fabric across multiple devices.

Required: Increased Bandwidth Available

The result of the fabric is that each edge device has the total bandwidth of the fabric available to every other edge device. This is one big difference from traditional data center designs; we aggregate the traffic by either stacking multiple streams onto a single link or carrying the streams serially.

Challenge: Oversubscription

With the traditional 3-tier design, we aggregate everything at the core, leading to oversubscription ratios that degrade performance. With the ACI Leaf and Spine design, we spread the load across all devices with equidistant endpoints, allowing us to carry the streams parallel.

Required: Routed Multipathing

Then, we have horizontal scaling load balancing.  Load balancing with this topology uses multipathing to achieve the desired bandwidth between the nodes. Even though this forwarding paradigm can be based on Layer 2 forwarding ( bridging) or Layer 3 forwarding ( routing), the ACI leverages a routed approach to the Leaf and Spine design, and we have Equal Cost Multi-Path (ECMP) for both Layer 2 and Layer 3 traffic. 

**Overlay and Underlay Design**

Mapping Traffic:

So you may be asking how we can have Layer 3 routed core and pass Layer 2 traffic. This is done using the overlay, which can map different traffic types to other overlays. So, we can have Layer 2 traffic mapped to an overlay over a routed core.

L3 active-active links: ACI links between the Leaf and the Spine switches are L3 active-active links. Therefore, we can intelligently load balance and traffic steer to avoid issues. We don’t need to rely on STP to block links or involve STP in fixing the topology.

Challenge: IP – Identity & Location

When networks were first developed, there was no such thing as an application moving from one place to another while it was in use. So, the original architects of IP, the communication protocol used between computers, used the IP address to indicate both the identity of a device connected to the network and its location on the network. Today, in the modern data center, we need to be able to communicate with an application or application tier, no matter where it is.

Required: Overlay Encapsulation

One day, it may be in location A and the next in location B, but its identity, which we communicate with, is the same on both days. An overlay is when we encapsulate an application’s original message with the location to which it needs to be delivered before sending it through the network. Once it arrives at its final destination, we unwrap it and deliver the original message as desired.

The identities of the devices (applications) communicating are in the original message, and the locations are in the encapsulation, thus separating the place from the identity. This wrapping and unwrapping is done on a per-packet basis and, therefore, must be done quickly and efficiently.

**Overlay and Underlay Components**

The Cisco SDN ACI has an overlay and underlay concept, which forms a virtual overlay solution. The role of the underlay is to glue together devices so the overlay can work and be built on top. So, the overlay, which is VXLAN, runs on top of the underlay, which is IS-IS. In the ACI, the IS-IS protocol provides the routing for the overlay, which is why we can provide ECMP from the Leaf to the Spine nodes. The routed underlay provides an ECMP network where all leaves can access Spine and have the same cost links. 

ACI overlay
Diagram: Overlay. Source Cisco

Underlay & Overlay Interaction

Example: 

Let’s take a simple example to illustrate how this is done. Imagine that application App-A wants to send a packet to App-B. App-A is located on a server attached to switch S1, and App-B is initially on switch S2. When App-A creates the message, it will put App-B as the destination and send it to the network; when the message is received at the edge of the network, whether a virtual edge in a hypervisor or a physical edge in a switch, the network will look up the location of App-B in a “mapping” database and see that it is attached to switch S2.

It will then put the address of S2 outside of the original message. So, we now have a new message addressed to switch S2. The network will forward this new message to S2 using traditional networking mechanisms. Note that the location of S2 is very static, i.e., it does not move, so using traditional mechanisms works just fine.

Upon receiving the new message, S2 will remove the outer address and thus recover the original message. Since App-B is directly connected to S2, it can easily forward the message to App-B. App-A never had to know where App-B was located, nor did the network’s core. Only the edge of the network, specifically the mapping database, had to know the location of App-B. The rest of the network only had to see the location of switch S2, which does not change.

Let’s now assume App-B moves to a new location switch S3. Now, when App-A sends a message to App-B, it does the same thing it did before, i.e., it addresses the message to App-B and gives the packet to the network. The network then looks up the location of App-B and finds that it is now attached to switch S3. So, it puts S3’s address on the message and forwards it accordingly. At S3, the message is received, the outer address is removed, and the original message is delivered as desired.

App-A did not track App-B’s movement at all. App-B’s address identified It, while the switch’s address, S2 or S3, identified its location. App-A can communicate freely with App-B no matter where It is located, allowing the system administrator to place App-B in any area and move it as desired, thus achieving the flexibility needed in the data center.

Multicast Distribution Tree (MDT)

We have a Multicast Distribution Tree MDT tree on top that is used to forward multi-destination traffic without having loops. The Multicast distribution tree is dynamically built to send flood traffic for specific protocols. Again, it does this without creating loops in the overlay network. The tunnels created for the endpoints to communicate will have tunnel endpoints. The tunnel endpoints are known as the VTEP. The VTEP addresses are assigned to each Leaf switch from a pool that you specify in the ACI startup and discovery process.

Normalize the transports

VXLAN tunnels in the ACI fabric normalize the transports in the ACI network. Therefore, traffic between endpoints can be delivered using the VXLAN tunnel, resulting in any transport network regardless of the device connecting to the fabric. 

So, using VXLAN in the overlay enables any network, and you don’t need to configure anything special on the endpoints for this to happen. The endpoints that connect to the ACI fabric do not need special software or hardware. The endpoints send regular packets to the leaf nodes they are connected to directly or indirectly. As endpoints come online, they send traffic to reach a destination.

Bridge Domains and VRF

Therefore, the Cisco SDN ACI under the hood will automatically start to build the VXLAN overlay network for you. The VXLAN network is based on the Bridge Domain (BD), or VRF ACI constructs deployed to the leaf switches. The Bridge Domain is for Layer 2, and the VRF is for Layer 3. So, as devices come online and send traffic to each other, the overlay will grow in reachability in the Bridge Domain or the VRF. 

Direct host routing for endoints

Routing within each tenant, VRF is based on host routing for endpoints directly connected to the Cisco ACI fabric. For IPv4, the host routing is based on the /32, giving the ACI a very accurate picture of the endpoints. Therefore, we have exact routing in the ACI.  In conjunction, we have a COOP database that runs on the Spines and offers remarkably optimized fabric to know where all the endpoints are located.

To facilitate this, every node in the fabric has a TEP address, and we have different types of TEPs depending on the device’s role. The Spine and the Leaf will have TEP addresses but will differ from each other.

COOP database
Diagram: COOP database

The VTEP and PTEP

The Leaf’s nodes are the Virtual Tunnel Endpoints (VTEP), which are also known as the physical tunnel endpoints (PTEP) in ACI. These PTEP addresses represent the “WHERE” in the ACI fabric where an endpoint lives. Cisco ACI uses a dedicated VRF and a subinterface of the uplinks from the Leaf to the Spines as the infrastructure to carry VXLAN traffic. In Cisco ACI terminology, the transport infrastructure for VXLAN traffic is known as Overlay-1, which is part of the tenant “infra.” 

**The Spine TEP**

The Spines also have a PTEP and an additional proxy TEP, which are used for forwarding lookups into the mapping database. The Spines have a global view of where everything is, which is held in the COOP database synchronized across all Spine nodes. All of this is done automatically for you.

**Anycast IP Addressing**

For this to work, the Spines have an Anycast IP address known as the Proxy TEP. The Leaf can use this address if they do not know where an endpoint is, so they ask the Spine for any unknown endpoints, and then the Spine checks the COOP database. This brings many benefits to the ACI solution, especially for traffic optimizations and reducing flooded traffic in the ACI. Now, we have an optimized fabric for better performance.

The ACI optimizations

**Mouse and elephant flow**

This provides better performance for load balancing different flows. For example, in most data centers, we have latency-sensitive flows, known as mouse flows, and long-lived bandwidth-intensive flows, known as elephant flows. 

The ACI has more precisely load-balanced traffic using algorithms that optimize mouse and elephant flows and distribute traffic based on flow lets: flow let load-balancing. Within a Leaf, Spine latency is low and consistent from port to port.

The max latency of a packet from one port to another in the architecture is the same regardless of the network size. So you can scale the network without degrading performance. Scaling is often done on a POD-by-POD basis. For more extensive networks, each POD would be its Leaf and Spine network.

**ARP optimizations: Anycast gateways**

The ACI comes by default with a lot of traffic optimizations. Firstly, instead of using an ARP and broadcasting across the network, that can hamper performance. The Leaf can assume that the Spine will know where the destination is ( and it does via the COOP database ), so there is no need to broadcast to everyone to find a destination.

If the Spine knows where the endpoint is, it will forward the traffic to the other Leaf. If not, it will drop it.

**Fabric anycast addressing**

This again adds performance benefits to the ACI solution as the table sizes on the Leaf switches can be kept smaller than they would if they needed to know where all the destinations were, even if they were not or never needed to communicate with them. On the Leaf, we have an Anycast address too.

These fabric Anycast addresses are available for Layer 3 interfaces. On the Leaf ToR, we can establish an SVI that uses the same MAC address on every ToR; therefore, when an endpoint needs to route to a ToR, it doesn’t matter which ToR you use. The Anycast Address is spread across all ToR leaf switches. 

**Pervasive gateway**

Now we have predictable latency to the first hop, and you will use the local route VRF table within that ToR instead of traversing the fabric to a different ToR. This is the Pervasive Gateway feature that is used on all Leaf switches. The Cisco ACI has many advanced networking features, but the pervasive gateway is my favorite. It does take away all the configuration mess we had in the past.

ACI Cisco: Integrations

  • Routing Control Platform

Then came along Cisco SDN ACI, the ACI Cisco, which operates differently from the traditional data center with an application-centric infrastructure. The Cisco application-centric infrastructure achieves resource elasticity with automation through standard policies for data center operations and consistent policy management across multiple on-premises and cloud instances.

  • Extending & Integrating the fabric

What makes the Cisco ACI interesting is its several vital integrations. I’m not talking about extending the data center with multi-pod and multi-site, for example, with AlgoSec, Cisco AppDynamics, and SD-WAN. AlgoSec enables secure application delivery and policy across hybrid network estates, while AppDynamic lives in a world of distributed systems Observability. SD-WAN enabled path performance per application with virtual WANs.

Cisco Multi-Pod Design

Cisco ACI Multi-Pod is part of the “Single APIC Cluster / Single Domain” family of solutions, as a single APIC cluster is deployed to manage all the interconnected ACI networks. These separate ACI networks are named “pods,” Each looks like a regular two-tier spine-leaf topology. The same APIC cluster can manage several pods, and to increase the resiliency of the solution, the various controller nodes that make up the cluster can be deployed across different pods.

ACI Multi-Pod
Diagram: Cisco ACI Multi-Pod. Source Cisco.

ACI Cisco and AlgoSec

With AlgoSec integrated with the Cisco ACI, we can now provide automated security policy change management for multi-vendor devices and risk and compliance analysis. The AlgoSec Security Management Solution for Cisco ACI extends ACI’s policy-driven automation to secure various endpoints connected to the Cisco SDN ACI fabric.

These simplify network security policy management across on-premises firewalls, SDNs, and cloud environments. They also provide visibility into ACI’s security posture, even across multi-cloud environments. 

ACI Cisco and AppDynamics 

Then, with AppDynamics, we are heading into Observability and controllability. Now, we can correlate app health and network for optimal performance, deep monitoring, and fast root-cause analysis across complex distributed systems with numbers of business transactions that need to be tracked.

This will give your teams complete visibility of your entire technology stack, from your database servers to cloud-native and hybrid environments. In addition, AppDynamics works with agents that monitor application behavior in several ways. We will examine the types of agents and how they work later in this post.

ACI Cisco and SD-WAN 

SD-WAN brings a software-defined approach to the WAN. These enable a virtual WAN architecture to leverage transport services such as MPLS, LTE, and broadband internet services. So, SD-WAN is not a new technology; its benefits are well known, including improving application performance, increasing agility, and, in some cases, reducing costs.

The Cisco ACI and SD-WAN integration makes active-active data center design less risky than in the past. The following figures give a high-level overview of the Cisco ACI and SD-WAN integration. For pre-information generic to SD-WAN, go here: SD-WAN Tutorial

SD WAN integration
Diagram: Cisco ACI and SD-WAN integration

The Cisco SDN ACI with SD-WAN integration helps ensure an excellent application experience by defining application Service-Level Agreement (SLA) parameters. Cisco ACI releases 4.1(1i) and adds support for WAN SLA policies. This feature enables admins to apply pre-configured policies to specify the packet loss, jitter, and latency levels for the tenant traffic over the WAN.

When you apply a WAN SLA policy to the tenant traffic, the Cisco APIC sends the pre-configured policies to a vManage controller. The vManage controller, configured as an external device manager that provides SDWAN capability, chooses the best WAN link that meets the loss, jitter, and latency parameters specified in the SLA policy.

Openshift and Cisco SDN ACI

OpenShift Container Platform (formerly known as OpenShift Enterprise) or OCP is Red Hat’s offering for the on-premises private platform as a service (PaaS). OpenShift is based on the Origin open-source project and is a Kubernetes distribution, the defacto for container-based virtualization. The foundation of the OpenShift networking SDN is based on Kubernetes and, therefore, shares some of the same networking technology along with some enhancements, such as the OpenShift route construct.

Other data center integrations

Cisco SDN ACI has another integration with Cisco DNA Center/ISE that maps user identities consistently to endpoints and apps across the network, from campus to the data center. Cisco Software-Defined Access (SD-Access) provides policy-based automation from the edge to the data center and the cloud.

Cisco SD-Access provides automated end-to-end segmentation to separate user, device, and application traffic without redesigning the network. This integration will enable customers to use standard policies across Cisco SD-Access and Cisco ACI, simplifying customer policy management using Cisco technology in different operational domains.

OpenShift and Cisco ACI

OpenShift does this with an SDN layer and enhances Kubernetes networking to create a virtual network across all the nodes. It is made with the Open Switch standard. For OpenShift SDN, this pod network is established and maintained by the OpenShift SDN, configuring an overlay network using a virtual switch called the OVS bridge. This forms an OVS network that gets programmed with several OVS rules. The OVS is a popular open-source solution for virtual switching.

OpenShift SDN plugin

We mentioned that you could tailor the virtual network topology to suit your networking requirements, which can be determined by the OpenShift SDN plugin and the SDN model you select. With the default OpenShift SDN, several modes are available. This level of SDN mode you choose is concerned with managing connectivity between applications and providing external access to them. Some modes are more fine-grained than others. The Cisco ACI plugins offer the most granular.

Integrating ACI and OpenShift platform

The Cisco ACI CNI plugin for the OpenShift Container Platform provides a single, programmable network infrastructure, enterprise-grade security, and flexible micro-segmentation possibilities. The APIC can provide all networking needs for the workloads in the cluster. Kubernetes workloads become fabric endpoints, like Virtual Machines or Bare Metal endpoints.

Cisco ACI CNI Plugin

The Cisco ACI CNI plugin extends the ACI fabric capabilities to OpenShift clusters to provide IP Address Management, networking, load balancing, and security functions for OpenShift workloads. In addition, the Cisco ACI CNI plugin connects all OpenShift Pods to the integrated VXLAN overlay provided by Cisco ACI.

Cisco SDN ACI and AppDynamics

AppDynamis overview

So, an application requires multiple steps or services to work. These services may include logging in and searching to add something to a shopping cart. These services invoke various applications, web services, third-party APIs, and databases, known as business transactions.

The user’s critical path

A business transaction is the essential user interaction with the system and is the customer’s critical path. Therefore, business transactions are the things you care about. If they start to go, your system will degrade. So, you need ways to discover your business transactions and determine if there are any deviations from baselines. This should also be automated, as learning baseline and business transitions in deep systems is nearly impossible using the manual approach.

So, how do you discover all these business transactions?

AppDynamics automatically discovers business transactions and builds an application topology map of how the traffic flows. A topology map can view usage patterns and hidden flows, acting as a perfect feature for an Observability platform.

AppDynamic topology

AppDynamics will automatically discover the topology for all of your application components. It can then build a performance baseline by capturing metrics and traffic patterns. This allows you to highlight issues when services and components are slower than usual.

AppDynamics uses agents to collect all the information it needs. The agent monitors and records the calls that are made to a service. This is from the entry point and follows executions along its path through the call stack. 

Types of Agents for Infrastructure Visibility

If the agent is installed on all critical parts, you can get information about that specific instance, which can help you build a global picture. So we have an Application Agent, Network Agent, and Machine Agent for Server visibility and Hardware/OS.

  • App Agent: This agent will monitor apps and app servers, and example metrics will be slow transitions, stalled transactions, response times, wait times, block times, and errors.  
  • Network Agent: This agent monitors the network packets, TCP connection, and TCP socket. Example metrics include performance impact Events, Packet loss, and retransmissions, RTT for data transfers, TCP window size, and connection setup/teardown.
  • Machine Agent Server Visibility: This agent monitors the number of processes, services, caching, swapping, paging, and querying. Example Metrics include hardware/software interrupts, virtual memory/swapping, process faults, and CPU/DISK/Memory utilization by the process.
  • Machine Agent: Hardware/OS – disks, volumes, partitions, memory, CPU. Example metrics: CPU busy time, MEM utilization, and pieces file.

Automatic establishment of the baseline

A baseline is essential, a critical step in your monitoring strategy. Doing this manually is hard, if not impossible, with complex applications. It is much better to have this done automatically. You must automatically establish the baseline and alert yourself about deviations from it.

This will help you pinpoint the issue faster and resolve it before it can be affected. Platforms such as AppDynamics can help you here. Any malicious activity can be seen from deviations from the security baseline and performance issues from the network baseline.

Summary: Cisco ACI Components

In the ever-evolving world of networking, organizations are constantly seeking ways to enhance their infrastructure’s performance, security, and scalability. Cisco ACI (Application Centric Infrastructure) presents a cutting-edge solution to these challenges. By unifying physical and virtual environments and leveraging network automation, Cisco ACI revolutionizes how networks are built and managed.

Understanding Cisco ACI Architecture

At the core of Cisco ACI lies a robust architecture that enables seamless integration between applications and the underlying network infrastructure. The architecture comprises three key components:

1. Application Policy Infrastructure Controller (APIC):

The APIC serves as the centralized management and policy engine of Cisco ACI. It provides a single point of control for configuring and managing the entire network fabric. Through its intuitive graphical user interface (GUI), administrators can define policies, allocate resources, and monitor network performance.

2. Nexus Switches:

Cisco Nexus switches form the backbone of the ACI fabric. These high-performance switches deliver ultra-low latency and high throughput, ensuring optimal data transfer between applications and the network. Nexus switches provide the necessary connectivity and intelligence to enable the automation and programmability features of Cisco ACI.

3. Application Network Profiles:

Application Network Profiles (ANPs) are a fundamental aspect of Cisco ACI. ANPs define the policies and characteristics required for specific applications or application groups. By encapsulating network, security, and quality of service (QoS) policies within ANPs, administrators can streamline the deployment and management of applications.

The Power of Network Automation

One of the most compelling aspects of Cisco ACI is its ability to automate network provisioning, configuration, and monitoring. Through the APIC’s powerful automation capabilities, network administrators can eliminate manual tasks, reduce human errors, and accelerate the deployment of applications. With Cisco ACI, organizations can achieve greater agility and operational efficiency, enabling them to rapidly adapt to evolving business needs.

Security and Microsegmentation with Cisco ACI

Security is a paramount concern for every organization. Cisco ACI addresses this by providing robust security features and microsegmentation capabilities. With microsegmentation, administrators can create granular security policies at the application level, effectively isolating workloads and preventing lateral movement of threats. Cisco ACI also integrates with leading security solutions, enabling seamless network enforcement and threat intelligence sharing.

Conclusion:

Cisco ACI is a game-changer in the realm of network automation and infrastructure management. Its innovative architecture, coupled with powerful automation capabilities, empowers organizations to build agile, secure, and scalable networks. By leveraging Cisco ACI’s components, businesses can unlock new levels of efficiency, flexibility, and performance, ultimately driving growth and success in today’s digital landscape.

kubernetes security best practice

Kubernetes Security Best Practice

by Blog

Kubernetes Securtiy Best Practices

In today's rapidly evolving technological landscape, Kubernetes has emerged as a powerful platform for managing containerized applications. However, with great power comes great responsibility, especially when it comes to security. In this blog post, we will explore essential best practices to ensure the utmost security in your Kubernetes environment.

Limiting Privileges: To kickstart our journey towards robust security, it is crucial to implement strict access controls and limit privileges within your Kubernetes cluster. Employing the principle of least privilege ensures that only authorized individuals or processes can access sensitive resources and perform critical actions.

Regular Updates and Patching: Keeping your Kubernetes cluster up to date with the latest security patches is a fundamental aspect of maintaining a secure environment. Regularly monitor for new releases, security advisories, and patches provided by the Kubernetes community. Promptly apply these updates to mitigate potential vulnerabilities and safeguard against known exploits.

Network Policies and Segmentation: Implementing proper network policies and segmentation is paramount for securing your Kubernetes cluster. Define robust network policies to control inbound and outbound traffic, restricting communication between pods and external entities. Employ network segmentation techniques to isolate critical workloads, reducing the attack surface and improving overall security posture.

Container Image Security: Containers play a vital role in the Kubernetes ecosystem, and ensuring the security of container images is of utmost importance. Embrace secure coding practices, regularly scan container images for vulnerabilities, and utilize image signing and verification techniques. Implementing image provenance and enforcing image validation policies are also effective measures to enhance container image security.

Logging, Monitoring, and Auditing: Establishing a comprehensive logging, monitoring, and auditing strategy is crucial for detecting and mitigating security incidents within your Kubernetes cluster. Configure centralized logging to gather and analyze logs from various components. Leverage monitoring tools and implement alerting mechanisms to promptly identify and respond to potential security breaches. Regularly audit and review activity logs to track any suspicious behavior.

Securing your Kubernetes environment requires a diligent and proactive approach. By implementing best practices such as limiting privileges, regular updates, network policies, container image security, and robust logging and monitoring, you can significantly enhance the security of your Kubernetes cluster. Stay vigilant, stay updated, and prioritize security to protect your valuable applications and data.

Matt Conran

Highlights: Kubernetes Securtiy Best Practices

The Move to the Cloud

– Cloud workloads are typically managed with Kubernetes as they move to the cloud. Kubernetes’ popularity can be attributed to its declarative nature: It abstracts infrastructure details and allows users to specify which workloads they want to run. App teams only need to set up configurations in Kubernetes to deploy their applications; they don’t need to worry about how workloads are deployed, where workloads run, or other details like networking.

– To achieve this abstraction, Kubernetes manages the creation, shutdown, and restart of workloads. As a typical implementation, workloads can be scheduled on any available network resource (physical host or virtual machine) based on their requirements. Kubernetes clusters consist of resources that run workloads.

– As needed, Kubernetes restarts unresponsive nodes based on the status of workloads (which are deployed as pods in Kubernetes). It also manages the network between pods and hosts and pod communication. Network plug-ins allow you to select the type of networking technology. Although the network plug-in has some configuration options, you cannot directly control networking behavior (either IP address assignment or scheduling).

An Orchestration Tool

Kubernetes has quickly become the de facto orchestration tool for deploying Kubernetes microservices and containers to the cloud. It offers a way of running groups of resources as a cluster and provides an entirely different abstraction level to single container deployments, allowing better management. From a developer’s perspective, it will enable the rolling out of new features that align with business demands while not worrying about the underlying infrastructure complexities.

Security Pitfalls: But there are pitfalls to consider when considering Kubernetes security best practices and Docker container security. Security teams must maintain the required visibility, compliance, and control, which is difficult because they do not control the underlying infrastructure. To help you on your security journey, you should introduce a chaos engineering project, specifically chaos engineering kubernetes.

This will help you find the breaking points and performance-related issues that may indirectly create security gaps in your Kubernetes cluster, making the cluster acceptable to Kubernetes attack vectors.

Before you proceed, you may find the following posts helpful:

  1. OpenShift SDN
  2. Hands On Kubernetes
  3. Kubernetes Network Namespace

 

Kubernetes Securtiy Best Practices

As workloads move to the cloud, Kubernetes is the most common orchestrator for managing them. Kubernetes’s popularity is due to its declarative nature: It abstracts infrastructure details and allows users to specify the workloads they want to run and the desired outcomes.

However, securing, observing, and troubleshooting containerized workloads on Kubernetes is challenging. It requires a range of considerations, from infrastructure choices and cluster configuration to deployment controls, runtime, and network security. Keep in mind that Kubernetes is not secure by default.

Kubernetes Architecture

Before we delve into some of Kubernetes’ security best practices and attack vectors, let’s recap Kubernetes Networking 101. Within Kubernetes, four common constructs will always be: pods, services, labels, and replication controllers. All constructs combine to create an entire application stack.

Pods

Pods are the smallest scheduling unit within a Kubernetes environment. They hold a set of closely related containers. Containers within a Pod share the same network namespace and must be installed on the same physical host. This enables processing locally without latency traversing from one physical host to another.

Labels

As I said, containers within a pod share the same network namespaces. As a result, the containers within a pod can reach each other’s ports on localhost. Therefore, we need to introduce a tagging mechanism known as labels. Labels offer another level of abstraction by tagging items as a group. They are essentially key-value pairs categorizing constructs. For example, labels distinguish containers as part of an application stack’s web or database tier.

Replication Controllers (RC)

Then, we have replication controllers. Their primary purpose is to manage the pods’ lifecycle and state by ensuring the desired state always matches the actual state. The replication controllers ensure the correct numbers are running by creating or removing pods at any time.

Services

Other standard Kubernetes components are services. Services represent groups of pods acting as one, allowing pods to access services in other pods without directing service-destined traffic to the pod IP. Pods are targeted by accessing a service that represents a group of pods. A service can be analogous to a load balancer in front of a pod accepting front-end service-destined traffic.

Kubernetes API server

Finally, the Kubernetes API server validates and configures data for the API objects, which include the constructs mentioned above—pods, services, and replication controllers, to name a few. Also, consider the practice of chaos engineering to understand your Kubernetes environment better and test for resilience and breaking points. Chaos engineering Kubernetes breaks your system to help you better understand it.

Listing Kubernetes Security Controls

  • Regularly Update Kubernetes:

Keeping your Kubernetes environment up to date is one of the fundamental aspects of maintaining a secure cluster. Regularly updating Kubernetes ensures you have the latest security patches and bug fixes, reducing the risk of potential vulnerabilities.

  • Enable RBAC (Role-Based Access Control):

Implementing Role-Based Access Control (RBAC) is crucial for controlling and restricting user access within your Kubernetes cluster. By assigning appropriate roles and permissions to users, you can ensure that only authorized personnel can perform specific operations, minimizing the chances of unauthorized access and potential security breaches.

  • Use Network Policies:

Kubernetes Network Policies allow you to define and enforce rules for network traffic within your cluster. Effectively utilizing network policies can isolate workloads, control ingress and egress traffic, and prevent unauthorized communication between pods. This helps reduce the attack surface and enhance the overall security posture of your Kubernetes environment.

  • Employ Pod Security Policies:

Pod Security Policies (PSPs) enable you to define security configurations and restrictions for pods running in your Kubernetes cluster. By defining PSPs, you can enforce policies like running pods with non-root users, disallowing privileged containers, and restricting host namespace sharing. These policies help prevent malicious activities and reduce the impact of potential security breaches.

  • Implement Image Security:

Ensuring the security of container images is crucial to protect your Kubernetes environment. Employing image scanning tools and registries that provide vulnerability assessments allows you to identify and address potential security issues within your images. Additionally, only using trusted and verified images from reputable sources reduces the risk of running compromised or malicious containers.

  • Monitor and Audit Cluster Activity:

Implementing robust monitoring and auditing mechanisms is essential for promptly detecting and responding to security incidents. By leveraging Kubernetes-native monitoring solutions, you can gain visibility into cluster activity, detect anomalies, and generate alerts for suspicious behavior. Furthermore, regularly reviewing audit logs helps identify potential security breaches and provides valuable insights for enhancing the security posture of your Kubernetes environment.

**Understanding GKE-Native Monitoring**

GKE-native monitoring provides a seamless and integrated way to collect, analyze, and visualize metrics and logs from your GKE clusters. By leveraging Google Cloud’s robust monitoring services like Stackdriver, GKE offers a comprehensive solution for monitoring the health and performance of your applications. With GKE-native monitoring, you gain valuable insights into resource utilization, latency, error rates, and more, enabling you to make data-driven decisions and ensure optimal performance.

Logging plays a crucial role in understanding your applications’ behavior and diagnosing issues. GKE-native logging allows you to capture and analyze logs from your GKE clusters effortlessly. With Stackdriver Logging, you can collect logs from various sources, including application logs, system logs, and even logs from managed services running on GKE. This unified logging platform provides powerful filtering, searching, and alerting capabilities, making troubleshooting issues easier and gaining deeper insights into your system.

Kubernetes Attack Vectors

The hot topic for security teams is improving Kubernetes security, implementing Kubernetes security best practices within the cluster, and making container networking deployments more secure. Kubernetes is a framework that provides infrastructure and features. However, thinking outside the box regarding Kubernetes’ best security practices would be best.

We are not saying that Kubernetes doesn’t have built-in security features to protect your application architecture! These include logging and monitoring, debugging and introspection, identity, and authorization. But ask yourself: Do these defaults cover all aspects of your security requirements? Remember that much of the complexity with Kubernetes can be abstract with OpenShift networking.

Traditional Security Constructs  

A: -) Traditional security constructs don’t work with containers, as we have seen with the introduction of Zero Trust Networking. Containers have a different security model as they are short-lived and immutable. For example, securing containers differs from securing a virtual machine (VM).

B: -) For one, the attack surface is smaller, as containers usually only live for about a week, as opposed to a VM that may stay online forever. As a result, we need to manage vulnerabilities and compliance throughout the container scheduler lifecycle—from build tools to image registries to production.

C: -) How do you measure security within a Kubernetes environment? No one can ever be 100% secure, but one should follow certain best practices to harden the gates against bad actors. You can have different levels of security within other parts of the Kubernetes domain. However, a general Kubernetes security best practice would be introducing as many layers of protection as possible between your Kubernetes environment and the bad actors to secure valuable company assets.

Hacker sophistication

Hackers are growing in sophistication. We started with script kiddies in the 1980s and are now entering a world of artificial intelligence ( AI ) and machine learning (ML) in the hands of bad actors. They have the tools to throw everything and anything at your Kubernetes environment, dynamically changing attack vectors on the fly. If you haven’t adequately secured the domain, once a bad actor gains access to your environment, they can move laterally throughout the application stack, silently accessing valuable assets.

Kubernetes Security Best Practice: Security 101

Now that you understand the most commonly used Kubernetes constructs let’s explore some of the best practices for central Kubernetes security and attack vectors.

**Kubernetes API**

Firstly, a bad actor can attack the Kubernetes API server to attack the cluster further. By design, each pod has a service account associated with it. The service account has full permissions and sufficient privileges to access the Kubernetes API server. A bad actor can get the token mounted in the pod and then use that token to gain access to the API server.

Unfortunately, once they can access the Kubernetes API server, they can get information, such as secrets, to compromise the cluster further. For example, a bad actor could access the database server where sensitive and regulatory data is held.

**Kubernetes API – Mitigate**

Another way to mitigate a Kubernetes API attack is to introduce role-based access control (RBAC). Essentially, RBAC gives rules to service accounts that can be assigned to the entire cluster. RBAC allows you to set permission on individual constructs such as pods and containers and then define rules based on personal use cases.

Another Kubernetes best practice would be to introduce an API server firewall. This firewall would work similarly to a standard firewall, where you can allow and block ranges of addresses. Essentially, this narrows the attack surface, hardening Kubernetes security.

**Secure pods with network policy**

Introducing ingress and egress network policies would allow you to block network access to certain pods. Only then can certain pods communicate with each other. For example, network policies can be set up to restrict the web front end from communicating directly to the database tier.

There is also a risk of a user implementing unsecured pods. In this case, you can have network policies in place so that if an unsecured pod does appear in the Kubernetes environment, the Kubernetes API will reject it.

Remember, left to its defaults, intra-cluster pod-to-pod communication is open. You may think a bad actor could sniff this traffic, but that’s harder to do than in everyday environments. A better strategy to harden Kubernetes security would be to use a network policy that only allows certain pods to communicate with each other.

**Container escape**

In the past, an unpatched kernel or a bug in the web front-end allowed a bad actor to exit a container and gain access to other containers or even to the Kubelet process itself. Once the bad actor gets access to the Kubelet client, it can access the API server, gaining a further foothold in the Kubernetes environment.

**Sandboxed Pods**

One way to mitigate a container escape would be to introduce sandboxed pods inside a lightweight VM. A sandboxed pod provides additional barriers to security as it lies above the kernel itself. Providing layers of protection above the kernel brings many Kubernetes security advantages. As the sandboxed pod runs inside a VM, an additional bug is required to break out of the container if there is a kernel exploit.

**Scanning containers**

For adequate Kubernetes security, you should know if your container image is vulnerable. Running containers with vulnerabilities exposes the entire system to attacks. Scanning all container images to discover and remove known vulnerabilities would be best. The scanning function should integrate runtime enforcement and remediation capabilities, enabling a secure and compliant SDLC (Software Development Life Cycle).

**Restrict access to Kubectl**

Kubectl is a powerful command-line interface for running commands against Kubernetes clusters. It allows you to create and update resources in a Kubernetes environment. Due to its potential, one should restrict access to this tool with firewall rules.

Disabling the Kubernetes dashboard is a must, as it has been part of some high-profile compromises. Previously, bad actors accessed a publicly unrestricted Kubernetes dashboard and privileged account credentials to access resources and mine cryptocurrency.

As Kubernetes adoption continues to grow, ensuring the security of your cluster becomes increasingly essential. By following these Kubernetes security best practices, you can establish a robust security foundation for your infrastructure and applications.

Regularly updating Kubernetes, enabling RBAC, utilizing network policies, employing pod security policies, implementing image security measures, and monitoring cluster activity will significantly enhance your security posture. Embracing these best practices will protect your organization’s sensitive data and provide peace of mind in today’s ever-evolving threat landscape.

Summary: Kubernetes Securtiy Best Practices

In today’s digital landscape, ensuring the security of your Kubernetes cluster has become more critical than ever. With the increasing adoption of containerization and the rise of cloud-native applications, safeguarding your infrastructure from potential threats is a top priority. This blog post guided you through some essential Kubernetes security best practices to help you fortify your cluster and protect it against possible vulnerabilities.

Implement Role-Based Access Control (RBAC)

RBAC is a fundamental security feature in Kubernetes that allows you to define and enforce fine-grained access control policies within your cluster. You can ensure that only authorized entities can access critical resources by assigning appropriate roles and permissions to users and service accounts. This helps mitigate the risk of unauthorized access and potential privilege escalation.

Section 2: Enable Network Policies

Network policies provide a powerful means of controlling inbound and outbound network traffic within your Kubernetes cluster. By defining and enforcing network policies, you can segment your applications, restrict communication between different components, and reduce the attack surface. This helps protect your cluster from potential lateral movement by malicious entities.

Regularly Update Kubernetes Components

Keeping your Kubernetes components up to date is crucial for maintaining a secure cluster. Regularly updating to the latest stable versions helps ensure you benefit from bug fixes, security patches, and new security features introduced by the Kubernetes community. Additionally, monitoring security bulletins and promptly addressing reported vulnerabilities is essential for maintaining a robust and secure cluster.

Container Image Security

Containers play a central role in Kubernetes deployments, and securing container images is vital to maintaining a secure cluster. Implementing a robust image scanning process as part of your CI/CD pipeline helps identify and mitigate vulnerabilities present in container images before they are deployed into production. Additionally, leveraging trusted image registries and employing image signing and verification techniques helps ensure the integrity and authenticity of your container images.

Monitor and Audit Cluster Activity

Monitoring and auditing the activity within your Kubernetes cluster is essential for detecting and responding to potential security incidents. Leveraging Kubernetes-native monitoring tools and incorporating security-specific metrics and alerts can provide valuable insights into your cluster’s health and security. Additionally, enabling audit logging and analyzing the generated logs can help identify suspicious behavior, track changes, and support forensic investigations if necessary.

Conclusion:

Securing your Kubernetes cluster is a multi-faceted endeavor that requires a proactive approach and adherence to best practices. By implementing RBAC, enabling network policies, regularly updating components, ensuring container image security, and monitoring cluster activity, you can significantly enhance the security posture of your Kubernetes environment. Remember, a well-protected cluster not only safeguards your applications and data but also instills confidence in your customers and stakeholders.

Kubernetes PetSets

Kubernetes Networking 101

by Blog

Kubernetes Networking 101

Kubernetes, the popular container orchestration platform, has revolutionized the way applications are deployed and managed. However, for newcomers, understanding Kubernetes networking can be a daunting task. In this blog post, we will delve into the basics of Kubernetes networking, demystifying concepts and shedding light on how communication flows between pods and services.

In order to understand Kubernetes networking, it's crucial to grasp the concept of pods and nodes. Pods are the basic building blocks of Kubernetes, comprising one or more containers that work together. Nodes, on the other hand, are the individual machines in a Kubernetes cluster that run these pods. We'll explore how pods and nodes interact and communicate with each other.

Container Networking Interface (CNI): To enable communication between pods, Kubernetes relies on a plugin called the Container Networking Interface (CNI). This section will explain the role of CNI and how it facilitates networking in Kubernetes clusters. We'll also discuss popular CNI plugins like Calico, Flannel, and Weave, highlighting their features and use cases.

Service Discovery and Load Balancing: One of the key features of Kubernetes networking is service discovery and load balancing. Services act as an abstraction layer, providing a stable endpoint for accessing pods. We'll delve into how services are created, how they discover pods, and how load balancing is achieved to distribute traffic effectively.

Network Policies and Security: In a production environment, network security is of utmost importance. Kubernetes offers network policies to control traffic flow and enforce security rules. This section will cover how network policies work, how to define them, and how they can be used to restrict communication between pods or namespaces.

Kubernetes networking forms the backbone of a well-functioning cluster, enabling seamless communication between pods and services. By understanding the basics of pods, nodes, CNI, service discovery, load balancing, and network policies, you can unlock the full potential of Kubernetes. Whether you're just starting out or seeking to deepen your knowledge, mastering Kubernetes networking is a valuable skill for any DevOps engineer or Kubernetes enthusiast.

Matt Conran

Highlights: Kubernetes Networking 101

Kubernetes Components

To comprehend Kubernetes networking, we must first delve into the world of Pods. A Pod represents the smallest unit in the Kubernetes ecosystem, encapsulating one or more containers. Despite sharing the same network namespace, each Pod possesses a unique IP address, enabling inter-Pod communication. However, it is crucial to understand how Pods communicate with each other within a cluster.

Services act as an abstraction layer, facilitating the discovery and load balancing of Pods. By defining a Service, developers can decouple applications from specific Pod IP addresses, as Services provide a stable endpoint for internal communication. Furthermore, Services enable external access to Pods through the use of NodePorts or LoadBalancers, making them an essential component for networking in Kubernetes.

Ingress, a powerful Kubernetes resource, allows for the exposure of HTTP and HTTPS routes to external traffic. By implementing Ingress, developers can define rules and routes, effectively managing inbound traffic to their applications. This flexible and scalable approach simplifies networking complexities and provides a seamless experience for end-users accessing Kubernetes services.

Kubernetes Clusters in GKE

#### Why Choose Google Cloud for Your Kubernetes Cluster?

Google Cloud offers a unique advantage when it comes to running Kubernetes clusters. As the original creators of Kubernetes, Google offers deep integration between Kubernetes and its cloud services. Google Kubernetes Engine (GKE) provides a fully managed Kubernetes service, allowing developers to focus more on building and less on managing infrastructure. With GKE, you get the benefit of automatic upgrades, patching, and scaling, all backed by Google’s robust infrastructure. This ensures high availability and security for your applications, making it an ideal choice for businesses looking to leverage Kubernetes without the operational overhead.

#### Setting Up Your Kubernetes Cluster on Google Cloud

Setting up a Kubernetes cluster on Google Cloud is a straightforward process, thanks to the streamlined interface and comprehensive documentation provided by Google. First, you need to create a Google Cloud project and enable the Kubernetes Engine API. After that, you can use the Google Cloud Console or the `gcloud` command-line tool to create a new cluster. Google Cloud provides various configuration options, allowing you to customize the cluster to fit your specific needs, whether that’s optimizing for cost, performance, or a balance of both.

#### Best Practices for Managing Kubernetes Clusters

Once your Kubernetes cluster is up and running, managing it effectively becomes crucial. Google Cloud offers several tools and features to help with this. It’s recommended to use Google Cloud’s monitoring and logging services to keep track of your cluster’s performance and health. Implementing automated scaling helps you handle varying workloads without manual intervention. Additionally, consider using Kubernetes namespaces to organize your resources efficiently, enabling better resource allocation and access control across your teams.

Google Kubernetes EngineUnderstanding Pods and Services

One of the fundamental building blocks of Kubernetes networking is the concept of Pods and Services. Pods are the smallest unit in the platform and house one or more containers. Understanding how Pods communicate with each other and external entities is crucial. On the other hand, services provide a stable endpoint for accessing a group of Pods. 

– Cluster Networking: A robust networking solution is required for Pods and Services to communicate seamlessly within a Kubernetes cluster. We’ll dive into the inner workings of cluster networking, discussing popular networking plugins such as Calico, Flannel, and Cilium. 

– Ingress and Load Balancing: Kubernetes offers Ingress and load-balancing capabilities when exposing applications to the outside world. In this section, we’ll demystify Ingress, its role in routing external traffic to Services, and how to configure it effectively. We’ll also explore load-balancing options to ensure optimal traffic distribution across Pods.

– Network Security and Policies: With the increasing complexity of modern applications, network security becomes paramount. Kubernetes provides robust mechanisms to enforce network policies and secure communication between Pods. We’ll discuss how to define and apply network policies effectively, ensuring only authorized traffic flows within the cluster.

**Kubernetes networking aims to solve the following problems**

  1. Container-to-container communications with high coupling
  2. A pod-to-pod communication system
  3. Communicating from a pod to a service
  4. External-to-service communications

A virtual bridge network is a private network that containers attach to in the Docker networking model. Containers are allocated private IP addresses, so containers running on different machines cannot communicate. Docker allows developers to proxy traffic across nodes by mapping host ports to container ports. Docker administrators, usually system administrators, avoid port clashes in this scenario. In Kubernetes networking, it is handled differently.

The Kubernetes Networking Model

Kubernetes’ native networking model is capable of supporting multi-host cluster networking. Pods can communicate with each other by default, regardless of their hosts. Kubernetes relies on the CNI project to comply with the following requirements:

  • Without NAT, all containers must be able to communicate with each other.
  • Containers and nodes can communicate without NAT.
  • The IP address of a container matches the IP address of those outside the container.

A pod is a unit of work in Kubernetes. Containers in pods are always scheduled and run “together” on the same node. It is possible to separate instances of a service into distinct containers using this connectivity. Developers may run services in one container and log forwarders in another. Having processes running in separate containers allows them to have separate resource quotas (e.g., “the log forwarder cannot use more than 512 MB of memory”). Reducing the scope necessary to build a container also separates container build and deployment machinery.

The Kubernetes History

Google released Kubernetes, an open-source cluster management tool, in June 2014. Google has said it launches over 2 billion containers per week, and Kubernetes was designed to control and manage the orchestration of all these containers and container networking. Initially, they built a Borg and Omega system, resulting in Kubernetes.

All lessons learned from Borg to Kubernetes are now passed to the open-source community. Kubernetes went 1.0 in July 2015 and is now at version 1.3.0. The Kubernetes deployment supports GCE, AWS, Azure, vSphere, and bare metal, and there are a variety of Kubernetes networking configuration parameters. Kubernetes forms the base for OpenShift networking. 

Kubernetes information check.

For additional information, before you go with Kubernetes networking 101, the post on Kubernetes chaos engineering discusses the need to stress and break a system, which is the only way to understand and optimize fully. Chaos Engineering starts with a baseline and introduces several controlled experiments. Then we have Kubernetes security best practice discuss the Kubernetes attack vectors and how to protect against them.

Understanding Pods & Service 

Before diving into deploying pods and services, it’s crucial to understand what a pod is within the context of Kubernetes. A pod is the smallest deployment unit in Kubernetes and can consist of one or more containers. Pods are designed to run a single instance of a specific application, sharing the same network and storage resources. By grouping containers, pods enable efficient communication and coordination between them.

Note: Deploying Pods

Now that we have a basic understanding of pods let’s explore the process of deploying one. To deploy a pod, you must create a YAML file describing its specifications, including the container image, resource requirements, and any necessary environment variables. Once the YAML file is created, you can use the `kubectl` command-line tool to apply the configuration and create the pod. It’s essential to verify the pod’s status using `kubectl get pods` and ensure it runs successfully.

While pods enable the deployment of individual containers, services expose these pods to the external world. Services act as an abstraction layer, allowing applications to communicate with pods without knowing their specific IP addresses or ports. Kubernetes offers various services, such as ClusterIP, NodePort, and LoadBalancer, each catering to different networking requirements.

Note: YAML File Specifications 

You must create another YAML file defining its specifications to deploy a service. This includes selecting the appropriate service type, specifying the target port and protocol, and associating it with the corresponding pod using labels. Once the YAML file is ready, you can use `kubectl` to create the service. By default, services are assigned a ClusterIP, which enables communication within the cluster. Depending on your needs, you may expose the service externally using NodePort or LoadBalancer.

Note: Kubernetes Scaling Abilities

One of Kubernetes’ key advantages is its ability to scale applications effortlessly. By adjusting the replica count in the deployment YAML file and applying the changes, Kubernetes automatically creates or terminates pods to maintain the desired replicas. Additionally, Kubernetes provides various commands and tools to monitor and manage deployments, allowing you to upgrade or roll back to previous versions easily.

Kubernetes Networking vs Docker Swarm

### Understanding Kubernetes Networking

Kubernetes, often hailed as the king of container orchestration, offers a robust and flexible networking model. At its core, Kubernetes assigns each pod its own IP address, providing a flat network structure. This means that Kubernetes does not require you to map ports between containers, simplifying communication. The Kubernetes network model supports various plugins through the Container Network Interface (CNI), allowing seamless integration with different cloud providers and on-premise systems. Moreover, Kubernetes supports complex networking policies, enabling fine-grained control over traffic flow and security.

### Docker Swarm’s Networking Simplicity

Docker Swarm, on the other hand, is known for its simplicity and ease of use. It provides an overlay network that enables services to communicate with each other across nodes seamlessly. Swarm’s networking is straightforward to set up and manage, making it appealing for smaller teams or projects with less complex networking needs. Docker Swarm also supports load balancing and service discovery out of the box. However, it lacks the extensive networking plugins and policy features available in Kubernetes, which might limit its scalability in larger, more complex environments.

### Performance and Scalability: A Comparative Analysis

When it comes to performance, both Kubernetes and Docker Swarm have their pros and cons. Kubernetes is designed for scalability, capable of handling thousands of nodes and pods. Its networking model is highly efficient, ensuring minimal latency in communication. Docker Swarm, while efficient for smaller clusters, might face challenges scaling to the level of Kubernetes. The simplicity of Swarm’s networking can become a bottleneck in environments that require more sophisticated networking configurations and optimizations. Thus, choosing between the two often depends on the scale and complexity of the deployment.

### Security Considerations

Security is a paramount concern in any networking setup. Kubernetes offers a range of security features, including network policies, secrets management, and role-based access control (RBAC). These features allow administrators to define and enforce security rules at a granular level. Docker Swarm, though simpler in its networking approach, provides basic security features such as mutual TLS encryption and node certificates. While adequate for many use cases, Swarm’s security features may not meet the needs of organizations with stricter compliance requirements.

Before you proceed, you may find the following posts helpful:

  1. OpenShift SDN
  2. Docker Default Networking 101
  3. OVS Bridge
  4. Hands-On Kubernetes

Kubernetes Networking 101

Google Cloud Data Centers

## Why Google Cloud for Kubernetes?

Google Cloud is a popular choice for running Kubernetes clusters due to its scalability, reliability, and integration with other Google Cloud services. Google Kubernetes Engine (GKE) allows users to quickly and easily deploy Kubernetes clusters, offering features like automatic scaling, monitoring, and seamless updates. With GKE, you can focus on developing your applications while Google handles the underlying infrastructure, ensuring your applications run smoothly.

## Setting Up Your First Kubernetes Cluster on Google Cloud

Deploying a Kubernetes cluster on Google Cloud involves several steps. First, you’ll need to set up a Google Cloud account and enable the Kubernetes Engine API. Then, using the Google Cloud Console or Cloud SDK, you can create a new Kubernetes cluster. Configuring your cluster involves selecting the appropriate machine types, defining node pools, and setting up networking and security policies. Once your cluster is set up, you can deploy applications, manage resources, and scale your infrastructure as needed.

## Best Practices for Managing Kubernetes Clusters

Effectively managing your Kubernetes clusters involves implementing several best practices. These include monitoring cluster performance using Google Cloud’s Stackdriver, automating deployments with CI/CD pipelines, and implementing robust security measures such as role-based access control (RBAC) and network policies. Additionally, regularly updating your clusters and applications ensures that you benefit from the latest features and security patches.

At a very high level, Kubernetes Networking 101 enables a group of hosts to be viewed as a single compute instance. The single compute instance, consisting of multiple physical hosts, is used to deploy containers. This offers an entirely different abstraction level to our single-container deployments.

Users start thinking about high-level application services and the concept of service design only. They are no longer concerned with individual container deployment, as the orchestrator looks after the deployment, scale, and management.

For example, if users specify to the orchestration system they want a specific type of application with defined requirements, now deploy it for me. The orchestrator manages the entire rollout, specifies the targeted hosts, and manages the container lifecycle. The user doesn’t get involved with host selection. This abstraction allows users to focus only on design and workload requirements – the orchestrator takes care of all the low-level deployment and management details.

Kubernetes Networking 101
Diagram: Kubernetes Networking 101

1. Pods:

Pods are the fundamental building blocks of Kubernetes. They consist of one or more containers that share a common network namespace. Each pod receives a unique IP address, allowing containers within the pod to communicate via localhost. However, communication between different pods requires additional networking components.

2. Services:

Services provide a stable and abstracted network endpoint to access a set of pods. By grouping pods based on a standard label, services ensure that applications can discover and communicate with each other seamlessly. Kubernetes offers four types of services: ClusterIP, NodePort, LoadBalancer, and ExternalName. Each service type caters to specific use cases, providing varying levels of accessibility.

3. Ingress:

Ingress is a Kubernetes resource that enables inbound connections to reach services within the cluster. It acts as a traffic controller, routing external requests to the appropriate service based on rules defined in the Ingress resource. Additionally, Ingress supports TLS termination, allowing secure communication with services.

Networking Concepts:

To comprehend Kubernetes networking fully, it is essential to grasp critical concepts that govern its behavior.

1. Cluster Networking:

Cluster networking refers to the communication between pods running on different nodes within a Kubernetes cluster. Kubernetes leverages various networking solutions, such as overlay networks and software-defined networking (SDN) to establish node connectivity. Popular SDN solutions include Calico, Flannel, and Weave.

2. DNS Resolution:

Kubernetes provides a built-in DNS service that enables easy discovery of services within the cluster. Each service is assigned a DNS name, which can be resolved to its corresponding IP address. This allows applications to communicate with services using their DNS names, enhancing flexibility and decoupling.

3. Network Policies:

Network policies define rules that dictate how pods communicate with each other. Administrators can enforce fine-grained access control and secure application traffic using network policies. Policies can be based on various criteria, such as IP addresses, ports, and protocols.

GKE Network Policies 

### Crafting Effective Network Policies

To create effective network policies, you must first grasp the structure and components that define them. Network policies in GKE are expressed in YAML format and consist of specifications that dictate how pods communicate with each other. This section will guide you through the essentials of crafting these policies, focusing on key elements such as pod selectors, ingress and egress rules, and the importance of defining explicit traffic paths.

### Implementing and Testing Your Policies

Once you have crafted your network policies, the next step is to implement and test them within your GKE environment. This section will provide a step-by-step guide on how to apply these policies and verify their effectiveness. We will cover common tools and commands used in testing, as well as strategies for troubleshooting and refining your policies to ensure they meet your desired outcomes.

### Best Practices for Network Policy Management

Managing network policies in a dynamic Kubernetes environment can be challenging. In this section, we’ll discuss best practices to streamline this process, including how to regularly audit and update your policies, integrate them with existing security frameworks, and utilize automation tools for enhanced management. Adopting these practices will help you maintain a secure and efficient network policy strategy in your GKE clusters.

Kubernetes network policy

Discussing Microservices

Distributed systems are more fine-grained now, with Kubernetes driving microservices. Microservices is a fast-moving topic involving the breaking down applications into many specific services. All services have their lifecycles, collaborating. Splitting the Monolith with microservices is not a new idea ( the term is ), but the emergence of new technologies has a profound effect.

The specific domains/containers require constant communication and access to each other’s services. Therefore, a strategy needs to be maintained to manage container interaction. For example, how do we scale containers? What’s the process for container failure? How do we react to container resource limits? 

Although Docker does help with container management, Kubernetes orchestration works on a different scale and looks at the entire application stack, allowing management at a service/application level.

We need a management and orchestration system to utilize containers’ and microservices’ portability fully. Containers can’t just be thrown into a sea of computing and expect to tie themselves together and work efficiently.

A management tool is required to govern and manage the life of containers, where they are placed, and to whom they can talk. Containers have a complicated existence, and many pieces are used to patch up their communication flow and management. We have updates, high availability, service discovery, patching, security, and networking. 

The most important aspect about Kubernetes or any content management system is that they are not concerned with individual container placement. Instead, the focus is on workload placement. Users enter high-level requirements, and the scheduler does the rest—where, when, and how many? 

Kubernetes networking 101 and network proximity.

Looking at workloads to analyze placement optimizes application deployment. For example, some processes that are part of the same service will benefit from network proximity. Front-end tiers sending large chunks of data to a backend database tier should be close to each other, not trombone across the network Kubernetes host for processing.

Likewise, when common data needs to be accessed and processed, it makes sense to put containers “close” to each other in a cluster. The following diagram displays the core Kubernetes architecture.

Kubernetes Networking 101: The Constructs

Kubernetes builds the application stack using four primary constructs: Pods, Services, Labels, and Replication Controllers. All constructs are configured and combined, creating a complete application stack with all management components—pods group similar containers on the same hosts.

Labels tag objects; replication controllers manage the desired state at a POD level, not container level, and services enable Pod-to-Pod communication. These constructs allow the management of your entire application lifecycle instead of individual application components. The construct definition is done through configuration files in YAML or JSON format.

The Kubernetes Pod

Pods are the smallest scheduling unit in Kubernetes and hold a set of closely related containers, all sharing fate and resources. Containers in a Pod share the same Kubernetes network namespace and must be installed on the same host. The main idea of keeping similar or related containers together is that processing is performed locally and does not incur any latency traversing from one physical host to another. As a result, local processing is always faster than remote processing. 

Pods essentially hold containers with related pieces of the application stack. The critical point is that they are ephemeral and follow a specific lifecycle. They should come and go without service interruption as any service-destined traffic directed should be towards the “service” endpoint IP address, not the Pod IP address.

Even Though Pods have a pod-wide-IP address, service reachability is carried out with service endpoints. Services are not as temporary ( although they can be deleted ) and don’t go away as much as Pods. They act as the front-end VIP to back-end Pods ( more on later ). This type of architecture hammers home the level of abstraction Kubernetes seeks.

Pod definition file

The following example displays a Pod definition file. We have basic configuration parameters, such as the Pod’s name and ID. Also, notice that the object type is set to “Pod.” This will be set according to the object we are defining. Later we will see this set as “service” for determining a service endpoint.

In this example, we define two containers – “testpod80” and “testpod8080”. We also have the option to specify the container image and Label. As Kubernetes assigns the same IP to the Pod where both containers live, we should be able to browse to the same IP but different port numbers, 80 or 8080. Traffic gets redirected to the respective container.

Kubernetes Pod

Kubernetes labels

Containers within a Pod share their network namespaces. All containers within can reach each other’s ports on localhost. This reduces the isolation between containers, but any more isolation would go against why we have Pods in the first place. They are meant to group “similar” containers sharing the same resource volumes, RAM, and CPU. For Pod segmentation, we have labels – a Kubernetes tagging system.

Labels offer another level of abstraction by tagging items as a group. They are essentially key-value pairs categorizing constructs. When we create Kubernetes constructs, we can set a label, which acts as a tag for that construct.

This means you can access a group of objects by specifying the label assigned to those objects. For example, labels distinguish containers as part of a web or database tier. The “selector” field tells Kubernetes which labels to use in finding Pods to forward traffic to.

Replication Controller

Container Scheduler

The replication controller ( RC ) manages the lifecycle and state of Pods. It ensures the desired state always matches the actual state. When you create an RC, you define how many copies ( aka replicas) of the Pod you want in the cluster.

The RC maintains that the correct numbers are running by creating or removing Pods at any time. Kubernetes doesn’t care about the number of containers running in a Pod; its only concern is the number of Pods. Therefore, it works at a Pod level.

The following is an example of an RC service definition file. Here, you can see that the desired state of replicas is “2.” A replica of 2 means that the number of pods each controller should maintain is 2. Changing the number up or down will either increase or decrease the number of Pods the replication controller manages.

For example, if the RC notices too many, it will stop some from returning the replication controller to the desired state. The RC keeps track of the desired state and returns it to the state specified in the service definition file. We may also assign a label for grouping replication controllers.

Kubernetes replication controller

Kubernetes Services

Service endpoints enable the abstraction of services and the ability to scale horizontally. Essentially, they are abstractions defining a logical set of Pods. Services represent groups of Pods acting as one, allowing Pods to access services in other Pods without directing service-destined traffic to the Pod IP. Remember, Pods are short-lived!

The service endpoint’s IP address is from the “Portal Net” range defined on the API service. The address is local to the host, so ensure it doesn’t clash with the docker0 bridge IP address.

Pods are targeted by accessing a service that represents a group of Pods. A service can be viewed with a similar analogy to a load balancer, sitting in front of Pods accepting front-end service-destined traffic. Services act as the main hooking point for service / Pod interactions. They offer high-level abstraction to Pods and the containers within.

All traffic gets redirected to the service IP endpoint, which redirects it to the correct backend. Traffic hits the service IP address ( Portal Net ), and a Netfilter IPtable rules forward to a local host high port number.

The proxy service creates a high port number, which forms the basis for load balancing. The load balancing object then listens to that port. The Kub proxy acts as a full proxy, maintaining two different TCP connections.

One separates the connection from the container to the proxy and another from the proxy to the load-balanced destination. The following is an example of a service definition file. The service listens on port 80 and sends traffic to the backend container port on 8080. Notice how the object kind is set to “service” and not “Pods” like in the previous definition file.

Kubernetes Services

Kubernetes Networking 101 Model

The Kubernetes networking model details that each Pod should have a routable IP address. This makes communication between Pods easier by not requiring any NAT or port mappings we had with earlier versions of Docker networking.

With Kubernetes, for example, we can have a web server and database server placed in the same Pod and use the local interface for cross-communication. Furthermore, there is no additional translation, so performance is better than that of a NAT approach.

Kubernetes network proxy

Kubernetes fulfills service -> Pods integration by enabling a network proxy called the Kube-proxy on every node in a cluster. The network proxy is always there, even if Pods are not running. Its main task is to route traffic to the correct Pod and can do TCP, UDP stream forwarding, or round-robin TCP, UDP forwarding.

The Kube proxy captures service-destination traffic and proxies requests from the service endpoint back to the application’s Pod. The traffic is forwarded to the Pods on the target port defined in the definition file, a random port assigned during service creation.

To make all this work, Kubernetes uses IPtables and Virtual IP addresses.

For example, when using Kubernetes alongside OpenContrail, the Kube-proxy is disabled on all hosts, and the OpenContrail router module implements connectivity via overlays ( MPLS over UDP encapsulation ). Another vendor on the forefront is Midokura, the co-founder behind OpenStack Project Kuryr. This project aims to bring any SDN plugin (MidoNet, Dragon flow, OVS, etc.) to Containers—more on these another time.

Kubernetes Pod-IP approach

The Pods IP address is reachable by all other Pods and hosts in the Kubernetes cluster. The address is not usually routable outside of the cluster. This should not be too much of a concern as most traffic stays within application tiers inside the cluster. Mapping external load-balancers achieves any inbound external traffic to services in the cluster.

The Pod-IP approach assumes that all Pods can reach each other without creating specific links. They can access each other by IP rather than through a port mapping on the physical host. Port mappings hide the original address by performing a masquerade – Source NAT.

Like your home residential router hides local PC and laptop IP addresses from the public Internet, cross-node communication is much simpler, as every Pod has an IP address. There isn’t any port mapping or NAT like there is with default docker networking. If the Kube proxy receives traffic for a Pod, not on its host, it simply forwards the traffic to the correct pod IP for that service.

The IP per POD offers a simplified approach to K8 networking. A unique IP per host would potentially need port mappings on the host IP as the number of containers increases. Managing port assignment would become an operational and management burden, similar to earlier versions of Docker. Conversely, a unique IP per container would surely hit scalability limits.

Kubernetes PAUSE container

Kubernetes has what’s known as a PAUSE container, also referred to as a Pod infrastructure container. It handles the networking by holding the networking namespace and IP address for the containers on that Pod. Some refer to the PAUSE container as an implementation detail you can safely ignore.

Each container uses a Pod’s “mapped container” mode to connect to the pause container. The mapped container mode is implemented with a source and target container grouping. The source container is the user-created container, and the target container is the infrastructure pause container.

Destination Pod IP traffic first lands on the pause container and gets translated to the backend containers. The pause container and the user-built containers all share the same network stack. Remember we created a service destination file with two containers – port 80 and port 8080? It is the pause container that listens on these port numbers.

In summary, the Kubernetes model introduces three methods of communication.

  • a) Pod-to-Pod communication directly by IP address. Kubernetes has a Pod-IP-wide metric simplifying communication.
  • b) Pod-to-Service Communication – Clients’ traffic is directed to the virtual service IP, which is then intercepted by the kub-proxy process ( running on all hosts) and directed to the correct Pod.
  • c) External-to-Internal Communication—External access is captured by an external load balancer that targets nodes in a cluster. The Kub proxy determines the correct Pod to send traffic to—more in a separate post.

Docker & Kubernetes networking comparison

Docker uses host-private networking. The Docker engine creates a default bridge, and every container gets a virtual ethernet to that bridge. The veth acts like a pipe – one end is mapped to the docker0 bridge namespace and the other to the container’s Linux namespace. This provides connectivity between containers on the same Docker bridge.

All containers are assigned an address from the 172.17.42.0 range and 172.17.42.1 to the default bridge acting as the container gateway. Any off-host traffic requires port mappings and NAT for communication. Therefore, the container’s IP address is hidden, and the network would see the container traffic coming from the docker nodes’ physical IP address. 

The effect is that containers can only talk to each other by IP address on the same virtual bridge. Any off-host container communication requires messy port allocations. Recently, there have been enhancements to docker networking and multi-host native connectivity without translations. Although there are enhancements to the Docker network, the NAT / Port mapping design is not a clean solution.

Docker Default networking
Diagram: Docker Default networking

The K8 model offers a different approach, and the docker0 bridge gets a routable IP address. Any outside host can access that Pod by IP address rather than through a port mapping on the physical host. Kubernetes has no NAT for container-to-container or container-to-node traffic.

Understanding Kubernetes networking is crucial for building scalable and resilient applications within a containerized environment. By leveraging its flexible architecture and components like Pods, Services, and Ingress, developers can enable seamless container communication and ensure efficient network management.

Moreover, comprehending network concepts like cluster networking, DNS resolution, and network policies empowers administrators to establish robust and secure communication channels within the Kubernetes ecosystem. Embracing Kubernetes networking capabilities unlocks the full potential of this powerful container orchestration platform.

Summary: Kubernetes Networking 101

Kubernetes has emerged as a powerful container orchestration platform, revolutionizing how applications are deployed and managed. However, understanding the intricacies of Kubernetes networking can be daunting for beginners. In this blog post, we will explore its essential components and concepts and dive into the fundamentals of Kubernetes networking.

Understanding Pods and Containers

To grasp Kubernetes networking, it is essential to comprehend the basic building blocks of this platform. Pods, the most minor deployable units in Kubernetes, consist of one or more containers that share the same network namespace. We will explore how containers within a pod communicate and how they are isolated from other pods.

Cluster Networking

Cluster networking enables communication between pods and services within a Kubernetes cluster. We will delve into different networking models, such as overlay and host-based networking, and discuss how they facilitate seamless communication between pods residing on other nodes.

Services and Service Discovery

Services act as an abstraction layer that enables pods to communicate with each other, regardless of their physical location within the cluster. We will explore various services, including ClusterIP, NodePort, and LoadBalancer, and understand how service discovery simplifies connecting to pods dynamically.

Ingress and Load Balancing

Ingress controllers provide external access to services within a Kubernetes cluster. We will discuss how ingress resources and controllers work together to route incoming traffic to the appropriate services, ensuring efficient load balancing and traffic management.

Conclusion: Kubernetes networking forms the backbone of seamless communication between containers and services within a cluster. By understanding the fundamental concepts and components of Kubernetes networking, beginners can confidently navigate the complexities of this powerful orchestration platform.

container based virtualization

Container Networking

by Blog

Container Networking

Containerization has revolutionized the way we develop, deploy, and manage applications. Organizations have gained newfound flexibility and scalability by encapsulating applications in lightweight, isolated containers. However, as the number of containers increases, so does the networking complexity among them. This blog post will explore container networking, its challenges, solutions, and best practices.

Container networking refers to the communication and connectivity between containers within a distributed system. Unlike traditional monolithic applications, containers are designed to be ephemeral and can be dynamically created, scaled, and destroyed. This dynamic nature necessitates a flexible and efficient networking infrastructure to facilitate seamless communication between containers, regardless of their physical location.

Container networking is the foundation upon which communication between containers and the outside world is established. It allows containers to connect with each other, with other services, and with external networks. In this section, we will cover the fundamental concepts of container networking, including network namespaces, bridges, and virtual Ethernet devices.

There are various networking models and architectures to consider when working with containers. From host networking to overlay networks, each model offers different benefits and trade-offs. We will explore these models in detail, discussing their use cases, advantages, and potential limitations.

While container networking brings flexibility and scalability, it also introduces certain challenges. In this section, we will address common obstacles faced when dealing with container networking, such as IP address management, network isolation, and service discovery. We will provide insights into overcoming these challenges and offer practical solutions.

To ensure smooth and efficient container networking, it is crucial to follow best practices. We will share a set of guidelines and recommendations for implementing container networking effectively. From choosing the appropriate network driver to configuring network security policies, these best practices will empower you to optimize your container networking infrastructure

Matt Conran

Highlights: Container Networking

Understanding Container Networking

Container networking refers to the process of establishing communication between containers and external networks. Unlike traditional networking methods, container networking provides isolation, scalability, and flexibility, making it ideal for modern application architectures. We can achieve better resource utilization and application performance by encapsulating applications and their dependencies within containers.

Container networking serves as the bridge that connects containers to each other and to external networks. It facilitates communication, data exchange, and resource sharing. This section will delve into the foundational concepts of container networking, covering topics such as network namespaces, virtual Ethernet devices, and bridge networks.

Key Points To Consider:

– Scalability and Resource Optimization: Container networking enables unprecedented scalability by allowing applications to be broken down into smaller, independent containers. These containers can be easily replicated and distributed across a cluster of machines, ensuring efficient resource utilization. With container networking, organizations can effortlessly scale their applications based on demand without incurring unnecessary costs or compromising performance.

– Enhanced Security and Isolation: One of the key advantages of container networking is the built-in security and isolation it offers. Each container operates within its own isolated environment, preventing any potential vulnerabilities from affecting other containers or the underlying host system. Container networking allows for the implementation of fine-grained access controls and network policies, ensuring that sensitive data and critical services remain safeguarded.

– Seamless Communication and Service Discovery: Container networking facilitates seamless communication between containers within and across different hosts. Containers can be connected through virtual networks, enabling them to exchange data and interact with each other effortlessly. Moreover, container orchestration platforms provide built-in service discovery mechanisms, allowing containers to locate and communicate easily with other services in the cluster, further simplifying the development and deployment process.

– Flexibility and Portability: Container networking offers unparalleled flexibility and portability, making it an ideal choice for modern application development. Containers can be easily moved or migrated between hosts, irrespective of the underlying infrastructure. This portability eliminates the need for tedious system configurations, making deployments swift and hassle-free. Furthermore, container networking enables developers to encapsulate the entire application stack, ensuring consistency across different environments, from development to production.

Connecting Containers

Container networking refers to establishing communication channels between containers and external resources, such as other containers, host machines, or the Internet. It allows containers to exchange data and access necessary services while maintaining isolation and security. By comprehending the basics of container networking, we can unlock its potential for building scalable and resilient applications.

Multiple networking models are available for containers, each with its advantages and use cases. We will explore three common models:

1. Bridge Networking: This model creates a bridge network interface on the host machine, enabling containers to communicate with each other through the bridge. It provides automatic DNS resolution and IP address assignment but lacks direct connectivity to the host network.

2. Overlay Networking: Overlay networks facilitate container communication on different hosts or multiple data centers. By encapsulating container traffic within virtual networks, overlay networking ensures seamless connectivity and flexibility, but it may introduce additional overhead.

3. Host Networking: This model allows containers to share the host network stack, leveraging its IP address and network interfaces. Host networking offers maximum performance but compromises container isolation and may lead to port conflicts.

Example: Docker Networking

 

GKE Network Policies

Why Network Policies Matter

The importance of network policies cannot be overstated. In a typical Kubernetes cluster, all pods can communicate with each other by default. While this might be convenient for development, it poses a significant security risk in production environments. Network policies provide a way to enforce rules that dictate which pods can communicate with each other. This level of control is crucial in maintaining a secure and robust microservices architecture. By implementing well-defined network policies, you can prevent potential attacks, such as lateral movement within the cluster, thus fortifying your application’s security posture.

### Crafting Effective Network Policies

Creating effective network policies requires a thorough understanding of your application’s architecture and communication patterns. Start by mapping out the data flow between your services. Identify which services need to communicate and which ones should be isolated. Use this information to define network policies that permit only the required traffic. When crafting these policies, it’s beneficial to follow the principle of least privilege—allow only what is necessary and deny everything else by default. This approach not only minimizes the attack surface but also simplifies policy management over time.

### Implementing Network Policies in GKE

Implementing network policies in GKE involves defining policy resources using YAML configuration files. These files specify the allowed ingress and egress rules for your pods. Begin by enabling network policy enforcement on your GKE cluster. Once enabled, you can apply your custom network policies using the `kubectl` command-line tool. It’s essential to test these policies in a controlled environment before deploying them to production. Regular audits and updates to your network policies are also crucial to adapt to changes in your application’s architecture and security requirements.

Kubernetes network policy

Understanding Docker’s Default Networking

– Docker’s default networking is based on a bridge network driver that creates a virtual network interface on the host machine. This bridge acts as a gateway, enabling containers to communicate with each other and the host. By default, Docker assigns IP addresses from a predefined range to containers, facilitating seamless connectivity within the network.

– One of the fundamental aspects of Docker’s default networking is container-to-container communication. Containers within the same bridge network can effortlessly communicate with each other using their respective IP addresses or container names. This opens up endless possibilities for building complex, interconnected systems composed of microservices.

– While container-to-container communication is vital, Docker also provides mechanisms to connect containers with the external world. We can expose services running inside containers to the host machine or the entire network by mapping container ports to host ports. This allows seamless integration of Dockerized applications with external systems.

– In addition to the default bridge network, Docker offers advanced networking techniques such as overlay networks. Overlay networks allow containers to communicate across multiple Docker hosts, enabling the creation of distributed systems and facilitating scalability. Understanding these advanced networking options expands the possibilities of using Docker in complex scenarios.

Container Orchestration

**Understanding Container Networking**

Container networking is a critical aspect of application deployment in GKE. It involves the communication between containers, nodes, and external services. In GKE, this process is streamlined with the integration of Kubernetes networking policies and the use of Google Cloud’s Virtual Private Cloud (VPC). These components work together to provide a secure and efficient networking environment, where each container can communicate with others while maintaining isolation and security.

**The Role of Kubernetes Networking Policies**

Kubernetes networking policies are essential for managing traffic flow within a GKE cluster. These policies define how pods communicate with each other and with external endpoints. By specifying rules for ingress and egress traffic, developers can fine-tune the security and performance of their applications. In GKE, networking policies are implemented using YAML configurations, providing a flexible and scalable approach to managing container networks.

**Integrating Google Cloud’s Virtual Private Cloud (VPC)**

Google Cloud’s VPC plays a pivotal role in enhancing the networking capabilities of GKE. With VPC, users can create isolated networks within Google Cloud, allowing for fine-grained control over IP address ranges, subnets, and routing. This integration ensures that containers within a GKE cluster can securely communicate with other Google Cloud services, on-premises resources, and the internet, while maintaining compliance with organizational security policies.

**Optimizing Performance with Container Networking**

Optimizing container networking in GKE involves balancing performance and security. By leveraging features like Network Endpoint Groups (NEGs) and Cloud Load Balancing, developers can ensure high availability and low latency for their applications. Additionally, monitoring tools provided by Google Cloud, such as Stackdriver, offer insights into network performance, enabling proactive management and troubleshooting of networking issues.

Google Kubernetes EngineUnderstanding Docker Swarm

At its core, Docker Swarm is a native clustering and orchestration solution for Docker containers. It enables the creation of a swarm, a group of Docker nodes that work together in a distributed system. Each node in the swarm can run multiple containers, forming a resilient and scalable infrastructure. By abstracting away the complexity of managing individual containers, Docker Swarm empowers developers and operators to focus on their applications’ logic rather than infrastructure intricacies.

Docker Swarm offers a plethora of features that streamline container deployment and management. Automatic load balancing, service discovery, and rolling updates are just a few of the capabilities that make Swarm an attractive choice for container orchestration. Additionally, Swarm provides fault tolerance, ensuring high availability even in the face of node failures. Its intuitive command-line interface and integration with Docker CLI make it easy to adopt and incorporate into existing workflows.

Benefits and Advantages

The advantages of Docker Swarm are manifold. Firstly, Swarm allows horizontal scaling, enabling applications to handle increased workloads effortlessly. Scaling up or down can be achieved seamlessly without downtime or disruptions. Furthermore, Swarm promotes fault tolerance through its replication and distribution mechanisms, ensuring that applications remain highly available even when faced with failures.

With built-in service discovery and load balancing, Swarm simplifies deploying and managing microservices architectures. Additionally, Swarm integrates well with other Docker tools and services, such as Docker Compose and Docker Registry, further enhancing its versatility.

What is Minikube?

Minikube is a lightweight, open-source tool that enables developers to run a single-node Kubernetes cluster locally. It provides a simplified way to set up and manage a Kubernetes environment on your machine, allowing developers to experiment, test, and develop applications without needing a full-scale production cluster. With Minikube, developers can replicate the production environment on their local machines, saving time and effort during development.

Example OpenShift: Network Services

The most common network service allows a source to reach an application endpoint. Nowadays, the network function no longer solely satisfies endpoint reachability; it is fully integrated into the application. In the case of OpenShift networking, the Route and Sevice construct provides both reachability and an abstraction layer for application access.

In the past, applications had three standard components: cache, web server, and database. Applications look very different now. Several services interact, are completely decoupled into units, and are packaged in containers; all are mobile and may move around.

Container Networking and the CNI

Running a container requires a host. On-premises data centers may use physical machines such as bare-metal servers, or virtual machines may be used in the cloud.

Docker daemon and client access interactive container registry. Containers can also be started, stopped, paused, and inspected, and container images pulled/pushed. Modern containers often comply with Open Container Initiative (OCI), and Docker is not the only option. Kubernetes and other alternatives to Docker can also be helpful.

Hosts and containers have a 1:N relationship. Typically, one host runs several containers. Facebook reports running 10 to 40 containers per host, depending on the machine’s beefiness.

You will likely have to deal with networking whether you use a single host or a cluster:

  • A single-host deployment almost always requires connecting to other containers on the same host; for example, WildFly might need to connect to a database.

  • During multi-host deployments, you must consider how containers communicate inside and between hosts. Your design decisions will likely be influenced by performance and security concerns. An Apache Spark or Apache Kafka cluster generally requires multiple hosts when a single host’s capacity is insufficient or for resilience reasons.

Docker networking

In a nutshell, Docker offers four single-host networking modes:

  • Bridge mode

This is the default network driver for apps running in standalone containers.

  • Host mode

It is also used for standalone containers, removing network isolation from the host.

  • Container mode

It lets you reuse another container’s network namespace. Used in Kubernetes.

  • No networking

It disables Docker networking support and allows you to set up custom networking.’

Recap: Container Security

Understanding Namespaces

Namespaces is a fundamental building block for achieving resource isolation within a Linux environment. They can virtualize system resources like process IDs, network interfaces, and file systems. By creating separate namespaces for different processes or groups of processes, we can ensure that each entity operates in its isolated environment, oblivious to other processes outside its namespace.

While namespaces focus on resource isolation, control groups take the concept further by enabling resource management. Control groups, commonly known as cgroups, allow administrators to allocate and limit system resources to specific processes or groups of processes, such as CPU, memory, and I/O. This fine-grained control enhances system performance, prevents resource starvation, and ensures fair distribution among entities.

Network Security for Docker

Securing the network connectivity of your Docker environment is essential to protect against potential attacks. Consider implementing these practices:

– Utilize Docker’s network security features, such as network segmentation and access control lists (ACLs).

– Enable and enforce firewall rules to control inbound and outbound traffic to and from containers.

– Consider using encrypted communication protocols (e.g., HTTPS) for containerized applications.

Docker Host Security

Securing the underlying Docker host is paramount for overall container security. Here are a few tips to enhance host security:

– Regularly update the host operating system and Docker daemon to patch known vulnerabilities.

– Employ strong access control measures to limit administrative privileges on the host.

– Implement intrusion detection and prevention systems to monitor and detect any unauthorized activities on the host.

container security

Understanding SELinux

SELinux is a mandatory access control (MAC) system that enforces fine-grained policies to restrict access and actions within a Linux system. It defines rules and labels for processes, files, and network resources, ensuring that only authorized activities are allowed.

When SELinux is enabled, it actively enforces access control policies on Docker containers and their associated network resources. SELinux labels define and implement rules regarding network communication, preventing unauthorized access or tampering.

One critical benefit of SELinux in Docker networking is its ability to mitigate network-based attacks. By leveraging SELinux’s access control capabilities, containers are isolated and protected from potential network threats. Unauthorized network interactions are blocked, reducing the attack surface and enhancing overall security.

Related: Before you proceed, you may find the following helpful:

  1. Container Based Virtualization
  2. Neutron Network

Container Networking

Docker Networking

The Docker networking model uses a virtual bridge network by default, defined per host, and a private network where containers attach. The container’s IP address is allocated a private IP address, which indicates containers operating on different machines cannot communicate with each other.

In this case, you will have to map host ports to container ports and then proxy the traffic to reach across nodes with Docker. Therefore, it is up to the administrator to avoid port clashes between containers. Kubernetes networking handles this differently.

Challenges in Container Networking:

Container networking presents several challenges that must be addressed to ensure optimal performance and reliability. Some of the key challenges include:

  • Network Isolation: Containers should be isolated from each other to prevent unauthorized access and potential security breaches.
  • IP Address Management: Containers are assigned unique IP addresses, which can quickly become challenging to manage as the number of containers grows.
  • Scalability: As the container ecosystem expands, the networking infrastructure must scale effortlessly to accommodate the increasing number of containers.
  • Service Discovery: Containers need a reliable mechanism to discover and communicate with other services within the network, especially in a microservices architecture.

Solutions and Best Practices:

To overcome these challenges, several solutions and best practices have emerged in the realm of container networking:

1. Container Network Interface (CNI): CNI is a specification that defines how container runtimes interact with networking plugins. It enables easy integration of various networking solutions into container orchestration platforms like Kubernetes and Docker.

2. Overlay Networking: Overlay networks create a virtual network that spans multiple hosts, allowing containers to communicate seamlessly, regardless of physical location. Technologies like VXLAN, GRE, and WireGuard are commonly used for overlay networking.

3. Network Policies: Network policies define the rules and restrictions for incoming and outgoing traffic between containers. By implementing network policies, organizations can enforce security and control network traffic flow within their containerized environments.

4. Service Mesh: Service mesh technologies, such as Istio and Linkerd, provide advanced networking capabilities, including traffic management, load balancing, and observability. They enhance the resilience and reliability of containerized applications by offloading complex networking tasks from individual services.

Service Mesh & Networking

### What is a Cloud Service Mesh?

A Cloud Service Mesh is designed to handle the complex communication needs between various microservices within a cloud-native application. It provides a unified way to secure, connect, and observe services without the need to modify the application code. By abstracting the network logic from the business logic, a service mesh ensures that services can communicate seamlessly and securely, regardless of the underlying infrastructure.

### The Role of Container Networking

Container networking refers to the methods and protocols used to enable communication between containerized applications. Containers, which package applications and their dependencies, need an efficient way to communicate to ensure smooth operation. This is where Cloud Service Mesh comes into play. It provides advanced networking capabilities such as load balancing, traffic management, and secure communication channels specifically designed for containers. By integrating with container orchestrators like Kubernetes, a service mesh can automate and optimize these networking tasks.

### Key Benefits of Using a Cloud Service Mesh

1. **Enhanced Security**: A service mesh can handle encryption and secure communication between services, ensuring that data remains protected as it travels across the network.

2. **Observability**: It provides insights into service performance and operational metrics, making it easier to diagnose issues and optimize performance.

3. **Traffic Management**: With features like traffic splitting, retries, and circuit breaking, a service mesh allows more granular control over how traffic flows between services.

4. **Resilience**: By managing retries and failovers, a service mesh can improve the overall resilience of applications, ensuring they remain available even during partial failures.

### Challenges and Considerations

While the benefits are compelling, implementing a Cloud Service Mesh is not without its challenges. Complexity in setup and management, potential performance overhead, and the need for specialized knowledge are some of the hurdles that organizations might face. It’s essential to evaluate whether the benefits outweigh these challenges in the context of your specific use case.

### Real-World Use Cases

Several leading organizations have already adopted Cloud Service Mesh to streamline their container networking. For instance, companies in the finance and healthcare sectors leverage service meshes to ensure secure and compliant communication between microservices. Meanwhile, tech giants use it to manage massive, distributed systems with ease, ensuring high availability and optimal performance.

Container Networking: A Different Application Philosophy

Computing is distributed over multiple elements, and they all interact arbitrarily. Network integration allows the application to be divided into several microservice components. Microservices will enable the application to be packaged into pieces and deployed on different hosts or even different cloud providers.

The application stack no longer belongs to a single server. Small, composable units enhance application replication and fault tolerance services. Containers and the ability to interconnect them make all this possible.

Containers offer a single-purpose environment. They are a bunch of lightweight namespaces and processes sharing a common kernel. Typically, you don’t run a full stack in a single container.

Ideally, there is only one process per container, which makes them very lightweight. VMs with guest O/S are resource-heavy; containers are a far better option if the application can be containerized.

However, containers offer an utterly different endpoint type for the network. With virtual machines spinning, they arrive and disappear quickly, measured in milliseconds, not seconds or minutes. The speed is down to their light properties. Some containerized application transactions only live for the length of transaction time. The infrastructure and network must be pre-built to support this type of endpoint.

Despite containerization’s advantages, remember that Docker container security and Docker security options are enabled at each point in the defense layer.

Introducing Docker Network Types

Docker Default Networking 101

Docker networking comes with several Docker network types and setups. The latest release is Docker version 1.10, which has enhancements, including linking with user-defined networks. There are other solutions available to enhance Docker networking functionality.

Docker is pluggable and allows ecosystem partners to plug into Docker networking. Project Calico offers a pure IP-based solution that utilizes the same principles of the Internet. Every host is an IP router. Calico uses a Felix agent and a BGP BIRD demon. This would be a clean option if the application only needs Layer 3 connectivity. 

The weave is another solution that operates an overlay function and aims to fit the multi-data center requirements. Each host in a Weave network thinks it belongs to one large switched fabric. The physical locations are abstracted, and they all have reachability. A multi-datacenter solution must concern itself with metrics other than endpoint reachability.

Container Networking with Linux Kernel and User Namespaces

Several unique resources, such as network interfaces and file systems, appear isolated inside each container even though the containers share the Linux kernel. Global resources are abstracted to appear unique per container, an abstraction made available using Linux namespaces.

Namespaces initially provided resource isolation for the first Linux containers project, offering a process virtualization solution. They do not create additional operating system instances on the host but instead use a single system with resource isolation.

Similarly, FreeBSD, where Jails provides resource isolation while running one kernel instance. In 2002, mount namespaces were the first type of Linux namespace with kernel 2.4.19. User namespaces emerged with kernel 3.8. 

The Different Namespaces

Containers have namespaces for each type of resource. We have six namespaces. 

    • Mount namespace makes the container feel like it has its filesystem. 
    • UTS namespace offers individual hostnames and domain names. 
    • User namespace provides isolation between the user and group IDs. 
    • IPC namespace isolates message queue systems. 
    • PID namespace offers different PIDs inside the container.

Finally, the network namespace gives the container a separate network stack. When you issue the docker ps command, you will see what ports are bound; these ports are on the namespace network interface.

Docker Networking and Docker Network Types

Install docker creates three network types – bridge, host, and none. You cannot delete these networks; you only interact with the default bridge network. There is the option to create user-defined networks and customized plugins. 

Network plugins (LibNetwork project) extend the docker network to support additional networking features such as IP VLAN or macvlan. User-defined networks can take the form of bridge or overlay networks.

Bridge networks have a single-host local scope, and overlay networks have a multi-host global scope. The diagram below displays the default bridge and the corresponding attached containers. The driver is “default,” meaning it has local scope.

Container Networking

The user-defined bridge is similar to the default bridge0. Containers from the same host are added and can cross-communicate. External access is not prohibited, but you can expose network sections with port mappings.

The user-defined overlay networking feature enables multi-host networking using the VXLAN networking driver libnetwork and Docker’s libkv library. The overlay function requires a valid key-value store.

The Docker libkv library supports Consul, Etcd, and ZooKeeper. With Docker default networking, a veth pair is created—one inside the container and the other outside in the namespaces. All are connected via the docker bridge. The veth is mapped to appear as eth0 in the container, using Linux namespaces.

Container Networking

Container networking, port mapping, and traffic flow.

Docker container networking cross-communicates if they are on the same machine and thus connect to the same virtual bridge. Containers can also connect to multiple networks at the same time. By default, containers on different machines can not reach each other. Cross-communication on different nodes must be allocated ports on the machine’s IP address, which are then proxied to the containers.

Port mapping provides access to the container from the outside. Docker allocates a DNAT port in the range of 49153 – 65535. This additional functionality continues to use the default Docker0 bridge but adds IPtables rules for the DNAT.

When you spin up a container and do a port mapping, you can see it inside the docker ps command that you have a port mapping from, for example, the host 8080 to container 80. IPtables is setting a port mapping between 8080 and the IP addresses assigned to the container.

The problem with Docker is that you may have to coordinate ports and plenty of NAT. NAT was designed to address the shortage of IPv4 addresses and was only meant to be used for a short period. It is so ingrained in people’s minds we still see it come out in fresh designs.

Ports and NAT are problematic at scale and expose users to cluster-level issues outside their control. This can lead to port conflicts and many complexities in scheduling. 

Kubernetes

Kubernetes networking does not use any NAT. Instead, it applies IP addresses at the Pod scope level. Remember that containers within a Pod share network namespaces, including their IP address. This means containers within a Pod can all reach each other’s ports on localhost. Kubernetes makes finding and configuring Kubernetes services much easier due to the unique IP addresses per Pod model.

Kubernetes Networking 101

Kubernetes Networking 101: IP-per-pod-model

Kubernetes network namespace has two fundamental abstractions – Pods and Services. Pods are essentially scheduling ATOMs in Kubernetes. They represent a group of tightly integrated containers that share resources and fate. An example of an application container grouping in a Pod might be a file puller and a web server.

Frontend / Backend tiers usually fall outside this category as they can be scaled separately. Pods share a network namespace and talk to each other as local hosts.

Pods are assigned a private IP that is routable within the internal fabric. Docker doesn’t give you an IP; you must do weird things like going through a host and exposing a port. This is not a great idea, as port deployment may have issues and operational complexities.

With Kubernetes, all containers talk to each other, even across nodes, without NAT. The entire solution is NAT-less, flat address space. Pods can talk to Pods without any translations. Communications on ports can be done but with well-known port numbers, avoiding service discovery systems like DNS-SD, Consul, or Etcd.

Understanding Pod Networking

At the heart of Kubernetes networking lies the concept of pods. Pods are the basic building blocks of any Kubernetes cluster, encapsulating one or more containers that work together. To ensure seamless communication between pods, Kubernetes assigns each pod a unique IP address and exposes it to other pods within the cluster. 

While pods enable communication within a cluster, services take it further by providing a stable endpoint for accessing a set of pods. There are various services in Kubernetes, including ClusterIP, NodePort, and LoadBalancer, with their use cases and how they enable service discovery.

Container network and services

The second abstraction is services. Services are similar to a load balancer. They are groups of Pods that act as one. It may be better to reference the service with an IP address, not a Pod. This is because Pods can go away, but services are more dedicated.

A typical flow would be something like this: a client on a cluster looks for IP for a particular service. The Kubernetes nodes that it is running on do an iptables DNAT. Instead of going to the service, it reroutes to the Kube proxy, which is a proxy running on every Kubernetes node.

It programs iptables rules to trap access to service IPs and redirect them to the backends using round-robin load balancing. It also watches the API server to determine which pods are active and ready to serve requests.

Several implementations include Google Compute Engine, Flannel, Calico, and OVS with GRE/VxLAN to support the IP-per-pod model. OpenVSwitch connects Pods on different hosts with GRE or VxLAN. The Linux bridge replaces the docker0 bridge, encapsulating traffic to and from Pods. Flannel may also be used with Kubernetes.

It creates an overlay network and gives a subnet to each host. Flannel can be used on cloud providers that cannot offer an entire /24 to each host. Flannels’ flannel agent runs on each host and controls the IP assignment. Calico, already mentioned, is also an IP-based solution that relies on traditional BGP.

 

Summary: Container Networking

Container networking is fundamental to modern software development and deployment, enabling seamless communication and connectivity between containers. In this blog post, we delved into the intricacies of container networking, exploring key concepts and best practices to simplify connectivity and enhance scalability.

Understanding Container Networking Basics

Container networking involves establishing communication channels between containers, allowing them to exchange data and interact. We will explore the underlying principles and technologies that facilitate container networking, such as bridge networks, overlay networks, and network namespaces.

Container Networking Models

Depending on your application’s specific requirements, you can choose from various container networking models. We will discuss popular models like host networking, bridge networking, and overlay networking, highlighting their strengths and use cases. Understanding these models will empower you to make informed decisions regarding your container networking architecture.

Networking Drivers and Plugins

Container runtimes like Docker provide networking drivers and plugins to enhance container networking capabilities. We will explore popular networking drivers, such as bridge, macvlan, and overlay, and delve into the benefits and considerations of each. Additionally, we will discuss third-party networking plugins that enable advanced features like network security, load balancing, and service discovery.

Best Practices for Container Networking

To ensure efficient and reliable container networking, it is essential to follow best practices. We will cover critical recommendations, including proper network segmentation, optimizing network performance, implementing security measures, and monitoring network traffic. These practices will help you maximize the potential of your containerized applications.

Challenges and Solutions

Container networking can present challenges like network congestion, scalability issues, and inter-container communication complexities. In this section, we will address these challenges and provide practical solutions. We will discuss techniques like service meshes, container orchestration frameworks, and software-defined networking (SDN) to overcome these obstacles effectively.

Conclusion:

Container networking is a critical component of modern application development and deployment. You can build robust and scalable containerized environments by understanding the basics, exploring various models, leveraging appropriate drivers and plugins, following best practices, and overcoming challenges. Embracing the power of container networking allows you to unlock the full potential of your applications, enabling efficient communication and seamless scalability.

container

Container Scheduler

by Blog

Container Scheduler

In modern application development and deployment, containerization has gained immense popularity. Containers allow developers to package their applications and dependencies into portable and isolated environments, making them easily deployable across different systems. However, as the number of containers grows, managing and orchestrating them becomes complex. This is where container schedulers come into play.

A container scheduler is a crucial component of container orchestration platforms. Its primary role is to manage the allocation and execution of containers across a cluster of machines or nodes. By efficiently distributing workloads, container schedulers ensure optimal resource utilization, high availability, and scalability.

Container schedulers serve as a crucial component in container orchestration frameworks, such as Kubernetes. They act as intelligent managers, overseeing the deployment and allocation of containers across a cluster of machines. By automating the scheduling process, container schedulers enable efficient resource utilization and workload distribution.
Enhanced Resource Utilization: Container schedulers optimize resource allocation by intelligently distributing containers based on available resources and workload requirements. This leads to better utilization of computing power, minimizing resource wastage.

Scalability and Load Balancing: Container schedulers enable horizontal scaling, allowing applications to seamlessly handle increased traffic and workload. With the ability to automatically scale up or down based on demand, container schedulers ensure optimal performance and prevent system overload.

High Availability: By distributing containers across multiple nodes, container schedulers enhance fault tolerance and ensure high availability. If one node fails, the scheduler automatically redirects containers to other healthy nodes, minimizing downtime and maximizing system reliability.
Microservices Architecture: Container schedulers are particularly beneficial in microservices-based applications. They enable efficient deployment, scaling, and management of individual microservices, facilitating agility and flexibility in development.
Cloud-Native Applications: Container schedulers are a fundamental component of cloud-native application development. They provide the necessary framework for deploying and managing containerized applications in dynamic and distributed environments.
DevOps and Continuous Deployment: Container schedulers play a vital role in enabling DevOps practices and continuous deployment. They automate the deployment process, allowing developers to focus on writing code while ensuring smooth and efficient application delivery.
Container schedulers have revolutionized the way organizations develop, deploy, and manage their applications. By optimizing resource utilization, enabling scalability, and enhancing availability, container schedulers empower businesses to build robust and efficient software systems. As technology continues to evolve, container schedulers will remain a critical tool in streamlining efficiency and scaling applications in the dynamic digital landscape.

Matt Conran

Highlights: Container Scheduler

Container Orchestration

– Orchestration and mass deployment tools are the first tools that add functionality to the Docker distribution and Linux container experience. Ansible Docker and New Relic’s Centurion tooling still function like traditional deployment tools but leverage the container as the distribution artifact. Their approach is pretty simple and easy to implement. Although Docker offers many benefits without much complexity, many of these tools have been replaced by more robust and flexible alternatives, like Kubernetes.

– In addition to Kubernetes or Apache Mesos with Marathon schedulers, fully automatic schedulers can manage a pool of hosts on your behalf. The free and commercial options ecosystems continue to grow rapidly, including HashiCorp’s Nomad, Mesosphere’s DC/OS (Datacenter Operating System), and Rancher.

– There is more to Docker than just a standalone solution. Despite its extensive feature set, someone will always need more than it can deliver alone. It is possible to improve or augment Docker’s functionality with various tools. Ansible for simple orchestration and Prometheus for monitoring the use of Docker APIs. Others take advantage of Docker’s plug-in architecture. Docker plug-ins are executable programs that receive and return data according to a specification.

**Virtualization**

Virtualization systems, such as VMware or KVM, allow you to run Linux kernels and operating systems on top of a virtualized layer, commonly called a hypervisor. On top of a hardware virtualization layer, each VM hosts its operating system kernel in a separate memory space, providing extreme isolation between workloads. A container is fundamentally different since it shares only one kernel and achieves all workload isolation within it. Operating systems are virtualized in this way.

**Docker and OCI Images**

There is almost no place today that does not use containers. Many production systems, including Kubernetes and most “serverless” cloud technologies, rely on Docker and OCI images as the packaging format for a significant and growing amount of software delivered into production environments.

Container Scheduling

Often, we want our containers to restart if they exit. Containers can come and go quickly, but some are very short-lived. You expect production applications, for example, to be constantly running after you tell them to do so. Schedulers may handle this for you if your system is more complex.

Docker’s cgroup-based CPU share constraints can have unexpected results, unlike VMs. Like the excellent command, they are relative limits, not hard limits. Suppose a container is limited to half the CPU share on a system that is not very busy. As the CPU is not busy, the CPU share limit would only have a limited effect since the scheduler pool is not competitive. Suddenly, the constraint will affect the first container when a second container using a lot of CPU is deployed to the same system. When allocating resources and constraining containers, keep this in mind.

  • Scheduling with Docker Swarm

Container scheduling lies at the heart of efficient resource allocation in containerized environments. It involves intelligently assigning containers to available resources based on various factors such as resource availability, load balancing, and fault tolerance. Docker Swarm simplifies this process by providing a built-in orchestration layer that automates container scheduling, making it seamless and hassle-free.

  • Scheduling with Apache Mesos

Apache Mesos is an open-source cluster manager designed to abstract and pool computing resources across data centers or cloud environments. As a distributed systems kernel, Mesos enables efficient resource utilization by offering a unified API for managing diverse workloads. With its modular architecture, Mesos ensures flexibility and scalability, making it a preferred choice for large-scale deployments.

  • Container Orchestration

Containerization has revolutionized software development by providing a consistent and isolated application environment. However, managing many containers across multiple hosts manually can be daunting. This is where container orchestration comes into play. Docker Swarm simplifies managing and scaling containers, making it easier to deploy applications seamlessly.

Docker Swarm offers a range of powerful features that enhance container orchestration. From declarative service definition to automatic load balancing and service discovery, Docker Swarm provides a robust platform for managing containerized applications. Its ability to distribute containers across a cluster of machines and handle failover seamlessly ensures high availability and fault tolerance.

Getting Started with Docker Swarm

You need to set up a Swarm cluster to start leveraging Docker Swarm’s benefits. This involves creating a Swarm manager, adding worker nodes, and joining them to the cluster. Docker Swarm provides a user-friendly command-line interface and APIs to manage the cluster, making it accessible to developers of all levels of expertise.

One of Docker Swarm’s most significant advantages is its ability to scale applications effortlessly. By leveraging the power of service replicas, Docker Swarm enables horizontal scaling, allowing you to handle increased traffic and demand. Swarm’s built-in load balancing also ensures that traffic is evenly distributed across containers, optimizing resource utilization.

Scheduling with Kubernetes

Kubernetes employs a sophisticated scheduling system to assign containers to appropriate nodes in a cluster. The scheduling process considers various factors such as resource requirements, node capacity, affinity, anti-affinity, and custom constraints. Using intelligent scheduling algorithms, Kubernetes optimizes resource allocation, load balancing, and fault tolerance.

Traditional Application

Applications started with single server deployments and no need for a container scheduler. However, this was an inefficient deployment model, yet it was widely adopted. Applications mapped to specific hardware do not scale. The landscape changed, and the application stack was divided into several tiers. Decoupling the application to a loosely coupled system is a more efficient solution. Nowadays, the application is divided into different components and spread across the network with various systems, dependencies, and physical servers.

Virtualization

**Example: OpenShift Networking**

An example of this is with OpenShift networking. OpenShift is based on Kubernetes and borrows many of the Kubernetes constructs. For pre-information, you may find this post informative on Kubernetes and Kubernetes Security Best Practice

**The Process of Decoupling**

The world of application containerization drives the ability to decouple the application. As a result, there has been a massive increase in containerized application deployments and the need for a container scheduler. With all these changes, remember the need for new security concerns to be addressed with Docker container security.

The Kubernetes team conducts regular surveys on container usage, and their recent figures show an increase in all areas of development, testing, pre-production, and production. Currently, Google initiates about 2 billion containers per week. Most of Google’s apps/services, such as its search engine, Docs, and Gmail, are packaged as Linux containers.

For pre-information, you may find the following helpful

  1. Kubernetes Network Namespace
  2. Docker Default Networking 101

Container Scheduler

With a container orchestration layer, we are marrying the container scheduler’s decisions on where to place a container with the primitives provided by lower layers. The container scheduler knows where containers “live,” and we can consider it the absolute source of truth concerning a container’s location.

So, a container scheduler’s primary task is to start containers on the most suitable host and connect them. It also has to manage failures by performing automatic fail-overs and be able to scale containers when there is too much data to process/compute for a single instance.

Popular Container Schedulers:

1. Kubernetes: Kubernetes is an open-source container orchestration platform with a powerful scheduler. It provides extensive features for managing and orchestrating containers, making it widely adopted in the industry.

2. Docker Swarm: Docker Swarm is another popular container scheduler provided by Docker. It simplifies container orchestration by leveraging Docker’s ease of use and integrates well with existing workflows.

3. Apache Mesos: Mesos is a distributed systems kernel that provides a framework for managing and scheduling containers and other workloads. It offers high scalability and fault tolerance, making it suitable for large-scale deployments.

Understanding Container Schedulers

Container schedulers, such as Kubernetes and Docker Swarm, play a vital role in managing containers efficiently. These schedulers leverage a range of algorithms and policies to intelligently allocate resources, schedule tasks, and optimize performance. By abstracting away the complexities of machine management, container schedulers enable developers and operators to focus on application development and deployment, leading to increased productivity and streamlined operations.

Key Scheduling Features

To truly comprehend the value of container schedulers, it is essential to understand their key features and functionality. These schedulers excel in areas such as automatic scaling, load balancing, service discovery, and fault tolerance. By leveraging advanced scheduling techniques, such as bin packing and affinity/anti-affinity rules, container schedulers can effectively utilize available resources, distribute workloads evenly, and ensure high availability of services.

Kubernetes & Docker Swarm

There are two widely used container schedulers: Kubernetes and Docker Swarm. Both offer powerful features and a robust ecosystem, but they differ in terms of architecture, scalability, and community support. By examining their strengths and weaknesses, organizations can make informed decisions on selecting the most suitable container scheduler for their specific requirements.

Kubernetes Clusters Google Cloud  

### Understanding Kubernetes Clusters

At the heart of Kubernetes is the concept of clusters. A Kubernetes cluster consists of a set of worker machines, known as nodes, that run containerized applications. Every cluster has at least one node and a control plane that manages the nodes and the workloads within the cluster. The control plane decisions, such as scheduling, are managed by a component called the Kubernetes scheduler. This scheduler ensures that the pods are distributed efficiently across the nodes, optimizing resource utilization and maintaining system health.

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### Google Cloud and Kubernetes: A Perfect Match

Google Cloud offers a powerful integration with Kubernetes through Google Kubernetes Engine (GKE). This managed service allows developers to deploy, manage, and scale their Kubernetes clusters with ease, leveraging Google’s robust infrastructure. GKE simplifies cluster management by automating tasks such as upgrades, repairs, and scaling, allowing developers to focus on building applications rather than infrastructure maintenance. Additionally, GKE provides advanced features like auto-scaling and multi-cluster support, making it an ideal choice for enterprises looking to harness the full potential of Kubernetes.

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### The Role of Container Schedulers

A critical component of Kubernetes is its container scheduler, which optimizes the deployment of containers across the available resources. The scheduler considers various factors, such as resource requirements, hardware/software/policy constraints, and affinity/anti-affinity specifications, to decide where to place new pods. This ensures that applications run efficiently and reliably, even as workloads fluctuate. By automating these decisions, Kubernetes frees developers from manual resource allocation, enhancing productivity and reducing the risk of human error.

Google Kubernetes EngineKey Features of Container Schedulers:

1. Resource Management: Container schedulers allocate appropriate resources to each container, considering factors such as CPU, memory, and storage requirements. This ensures that containers operate without resource contention, preventing performance degradation.

2. Scheduling Policies: Schedulers implement various scheduling policies to allocate containers based on priorities, constraints, and dependencies. They ensure containers are placed on suitable nodes that meet the required criteria, such as hardware capabilities or network proximity.

3. Scalability and Load Balancing: Container schedulers enable horizontal scalability by automatically scaling up or down the number of containers based on demand. They also distribute the workload evenly across nodes, preventing any single node from becoming overloaded.

4. High Availability: Schedulers monitor the health of containers and nodes, automatically rescheduling failed containers to healthy nodes. This ensures that applications remain available even in node failures or container crashes.

Benefits of Container Schedulers:

1. Efficient Resource Utilization: Container schedulers optimize resource allocation, allowing organizations to maximize their infrastructure investments and reduce operational costs by eliminating resource wastage.

2. Improved Application Performance: Schedulers ensure containers have the necessary resources to operate at their best, preventing resource contention and bottlenecks.

3. Simplified Management: Container schedulers automate the deployment and management of containers, reducing manual effort and enabling faster application delivery.

4. Flexibility and Portability: With container schedulers, applications can be easily moved and deployed across different environments, whether on-premises, in the cloud, or in hybrid setups. This flexibility allows organizations to adapt to changing business needs.

Containers – Raising the Abstraction Level

Container networking raises the abstraction level. The abstraction level was at a VM level, but with containers, the abstraction is moved up one layer. So, instead of virtual hardware, you have an idealized O/S stack.

1 -)  Containers change the way applications are packaged. They allow application tiers to be packaged and isolated, so all dependencies are confined to individual islands and do not conflict with other stacks. Containers provide a simple way to package all application pieces into an easily deployable unit. The ability to create different units radically simplifies deployment.

2 -) It creates a predictable isolated stack with ALL userland dependencies. Each application is isolated from others, and dependencies are sealed in. Dependencies are the natural killer as they can slow down deployment lifecycles. Containers combat this and fundamentally change the operational landscape. Docker and Rocket are the main Linux application container stacks in production.

3 -) Containers don’t magically appear. They need assistance with where to go; this is the role of the container scheduler. The scheduler’s main job is to start the container on the correct host and connect it. In addition, the scheduler needs to monitor the containers and deal with container/host failures.

4 -) The schedulers are Docker Swarm, Google Kubernetes, and Apache Mesos. Docker Swarm is probably the easiest to start with, and it’s not attached to any cloud provider. The container sends several requirements to the cluster scheduler. For example, I have this amount of resources and want to run five copies of this software with this amount of CPU and disk space – now find me a place.

Kubernetes – Container scheduler

Hand on Kubernetes. Kubernetes is an open-source cluster solution for containerized environments. It aims to make deploying microservice-based applications easy by using the concepts of PODS and LABELS to group containers into logical units. All containers run inside a POD.

PODS are the main difference between Kubernetes and other scheduling solutions. Initially, Kubernetes focused on continuously running stateless and “cloud native” stateful applications. In the coming future, it is said to support other workload types.

container scheduler

Kubernetes Networking 101

Kubernetes is not just interested in the deployment phase but works across the entire operational model—scheduling, updating, maintenance, and scaling. Unlike orchestration systems, it actively ensures the state matches the user’s requirements. Kubernetes is also involved in monitoring and healing if something goes wrong.

The Google team refers to this as a flight control mechanism. It provides the cluster and the decoupling between it. The application containers view the world as a sea of computing, an entirely homogenous (similar kind) cluster. Every machine you create in your fleet looks the same. The application is completely decoupled from low-level computing.

The unit of work has changed

The user does not need to care about physical placement anymore. The unit of work has changed and become a service. The administrator only needs to care about services, such as the amount of CPU, RAM, and disk space. The unit of work presented is now at a service level. The physical location is abstracted, all taken care of by the Kubernetes components.

This does not mean that the application components can be spread randomly. For example, some application components require the same host. However, selecting the hosts is no longer the user’s job. Kubernetes provides an abstracted layer over the infrastructure, allowing this type of management.

Containers are scheduled using a homogenous pool of resources. The VM disappears, and you think about resources such as CPU and RAM. Everything else, like location, disappears.

Kubernetes pod and label

The main building blocks for Kubernetes clusters are PODS and LABELS. So, the first step is to create a cluster, and once complete, you can proceed to PODS and other services. The diagram below shows the creation of a Kubernetes cluster. It consists of a 3-node instance created in us-east1-b.

containers

A POD is a collection of applications running within a shared context. Containers within a POD share fate and some resources, such as volumes and IP addresses. They are usually installed on the same host. When you create a POD, you should also make a kubernetes replication controller.

It monitors POD health and starts new PODS as required. Most PODS should be built with a replication controller, but it may not be needed if your POD is short-lived and is writing non-persistent data that won’t survive a restart. There are two types of PODS a) single container and b) Multi-container.

The following diagram displays the full details of a POD named example-tglxm. Its label is run=example and is located in the default network (namespace).

Container POD

A POD may contain either a single container with a private volume or a group with a shared volume. If a container fails within a POD, the Kubelet automatically restarts it. However, if an entire POD or host fails, the replication controller needs to restart it.

Replication to another host must be specifically configured. It is not automatic by default. The Kubernetes replication controller dynamically resizes things and ensures that the required number of PODS and containers are running. If there are too many, it will kill some; if not enough, it will start some more.

Kubernetes operates with the concept of LABELS – a key-value pair attached to objects, such as a POD. A label is a tag that can be used to query against. Kubernetes is an abstraction, and you can query whatever item you want using a label in an entire cluster.

For example, you can select all frontend containers with a label called “frontend”; it then selects all front ends. The cluster can be anywhere. Labels can also be building blocks for other services, such as port mappings. For example, a POD whose labels match a specific service selector is accessible through the defined service’s port.

Summary: Container Scheduler

Container scheduling plays a crucial role in modern software development and deployment. It efficiently manages and allocates resources to containers, ensuring optimal performance and minimizing downtime. In this blog post, we explored the world of container scheduling, its importance, key strategies, and popular tools used in the industry.

Understanding Container Scheduling

Container scheduling involves orchestrating the deployment and management of containers across a cluster of machines or nodes. It ensures that containers run on the most suitable resources while considering resource utilization, scalability, and fault tolerance factors. By intelligently distributing workloads, container scheduling helps achieve high availability and efficient resource allocation.

Key Strategies for Container Scheduling

1. Load Balancing: Load balancing evenly distributes container workloads across available resources, preventing any single node from being overwhelmed. Popular load-balancing algorithms include round-robin and least connections.

2. Resource Constraints: Container schedulers consider resource constraints such as CPU, memory, and disk space when allocating containers. By understanding the resource requirements of each container, schedulers can make informed decisions to avoid resource bottlenecks.

3. Affinity and Anti-Affinity: Schedulers can leverage affinity rules to ensure containers with specific requirements are placed together on the same node. Conversely, anti-affinity rules can separate containers that may interfere with each other.

Popular Container Scheduling Tools

1. Kubernetes: Kubernetes is a leading container orchestration platform with robust scheduling capabilities. It offers advanced features like auto-scaling, rolling updates, and cluster workload distribution.

2. Docker Swarm: Docker Swarm is a native clustering and scheduling tool for Docker containers. It simplifies the management of containerized applications and provides fault tolerance and high availability.

3. Apache Mesos: Mesos is a flexible distributed systems kernel that supports multiple container orchestration frameworks. It provides fine-grained resource allocation and efficient scheduling across large-scale clusters.

Conclusion:

Container scheduling is critical to modern software deployment, enabling efficient resource utilization and improved performance. Organizations can optimize their containerized applications by leveraging strategies like load balancing, resource constraints, and affinity rules. Furthermore, popular tools like Kubernetes, Docker Swarm, and Apache Mesos offer powerful scheduling capabilities to manage container deployments effectively. Embracing container scheduling technologies empowers businesses to scale their applications seamlessly and deliver high-quality services to end-users.