WAN Design Requirements

DMVPN

DMVPN

Cisco DMVPN is based on a virtual private network (VPN), which provides private connectivity over a public network like the Internet. Furthermore, the DMVPN network takes this VPN concept further by allowing multiple VPNs to be deployed over a shared infrastructure in a manageable and scalable way.

This shared infrastructure, or “DMVPN network,” enables each VPN to connect to the other VPNs without needing expensive dedicated connections or complex configurations.

DMVPN Explained: DMVPN creates a virtual network built on the existing infrastructure. This virtual network consists of “tunnels” between various endpoints, such as corporate networks, branch offices, or remote users. This virtual network allows for secure communication between these endpoints, regardless of their geographic location. As we are operating under an underlay, DMVPN is an overlay solution.

Hub Router:The hub router serves as the central point of connectivity in a DMVPN deployment. It acts as a central hub for all the spoke routers, enabling secure communication between them. The hub router is responsible for managing the dynamic IPsec tunnels and facilitating efficient routing.

Spoke Routers: Spoke routers are the remote endpoints in a DMVPN network. They establish IPsec tunnels with the hub router to securely transmit data. Spoke routers are typically located in branch offices or connected to remote workers' devices. They dynamically establish tunnels based on network requirements, ensuring optimal routing.

Next-Hop Resolution Protocol (NHRP):NHRP is a critical component of DMVPN that aids in dynamic IPsec tunnel establishment. It assists spoke routers in resolving the next-hop addresses for establishing tunnels with other spoke routers or the hub router. NHRP maintains a mapping database that allows efficient routing and simplifies network configuration.

Scalability:DMVPN offers excellent scalability, making it suitable for organizations with expanding networks. As new branch offices or remote workers join the network, DMVPN dynamically establishes tunnels without the need for manual configuration. This scalability eliminates the complexities associated with traditional point-to-point VPN solutions.

Cost Efficiency:By utilizing DMVPN, organizations can leverage affordable public network infrastructures instead of costly dedicated connections. DMVPN makes efficient use of bandwidth, reducing operational costs while providing secure and reliable connectivity.

Flexibility:DMVPN provides flexibility in terms of network design and management. It supports different routing protocols, allowing seamless integration with existing network infrastructure. Additionally, DMVPN supports various transport technologies, including MPLS, broadband, and cellular, enabling organizations to choose the most suitable option for their needs.

Highlights: DMVPM

VPN-based security solutions

VPN-based security solutions are increasingly popular and have proven effective and secure technology for protecting sensitive data traversing insecure channel mediums, such as the Internet.

Traditional IPsec-based site-to-site, hub-to-spoke VPN deployment models must scale better and be adequate only for small- and medium-sized networks. However, as demand for IPsec-based VPN implementation grows, organizations with large-scale enterprise networks require scalable and dynamic IPsec solutions that interconnect sites across the Internet with reduced latency while optimizing network performance and bandwidth utilization.

Scaling traditional IPsec VPN

Dynamic Multipoint VPN (DMVPN) technology scales IPsec VPN networks by offering a large-scale deployment model that allows the network to expand and realize its full potential. In addition, DMVPN offers scalability that enables zero-touch deployment models.

ipsec tunnel
Diagram: IPsec Tunnel

Encryption is supported through IPsec, making DMVPN a popular choice for connecting different sites using regular Internet connections. It’s a great backup or alternative to private networks like MPLS VPN. A popular option for DMVPN is FlexVPN.

Routing Technique

DMVPN (Dynamic Multipoint VPN) is a routing technique for building a VPN network with multiple sites without configuring all devices statically. It’s a “hub and spoke” network in which the spokes can communicate directly without going through the hub.

Advanced:

DMVPN Phase 2

DMVPN Phase 2 is an enhanced version of the initial DMVPN implementation. It introduces the concept of Next Hop Resolution Protocol (NHRP), which provides dynamic mapping between the participating devices’ physical and virtual IP addresses. This dynamic mapping allows for efficient and scalable communication within the DMVPN network.

Resolutions triggered by the NHRP

Learning the mapping information required through NHRP resolution creates a dynamic spoke-to-spoke tunnel. How does a spoke know how to perform such a task? As an enhancement to DMVPN Phase 1, spoke-to-spoke tunnels were first introduced in Phase 2 of the network. Phase 2 handed responsibility for NHRP resolution requests to each spoke individually, which means that spokes initiated NHRP resolution requests when they determined a packet needed a spoke-to-spoke tunnel.

Cisco Express Forwarding (CEF) would assist the spoke in making this decision based on information contained in its routing table.

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

  1. VPNOverview
  2. Dynamic Workload Scaling
  3. DMVPN Phases
  4. IPSec Fault Tolerance
  5. Dead Peer Detection
  6. Network Overlays
  7. IDS IPS Azure
  8. SD WAN SASE
  9. Network Traffic Engineering

DMVPM

Cisco DMVPN

♦DMVPN Components Involved

The DMVPN solution consists of a combination of existing technologies so that sites can learn about each other and create dynamic VPNs. Therefore, efficiently designing and implementing a Cisco DMVPN network requires thoroughly understanding these components, their interactions, and how they all come together to create a DMVPN network.

These technologies may seem complex, and this post aims to simplify them. First, we mentioned that DMVPN has different components, which are the building blocks of a DMVPN network. These include Generic Routing Encapsulation (GRE), Next Hop Redundancy Protocol (NHRP), and IPsec.

The Dynamic Multipoint VPN (DMVPN) feature allows users to better scale large and small IP Security (IPsec) Virtual Private Networks (VPNs) by combining generic routing encapsulation (GRE) tunnels, IPsec encryption, and Next Hop Resolution Protocol (NHRP).

Each of these components needs a base configuration for DMVPN to work. Once the base configuration is in place, we have a variety of show and debug commands to troubleshoot a DMVPN network to ensure smooth operations.

There are four pieces to DMVPN:

  • Multipoint GRE (mGRE)
  • NHRP (Next Hop Resolution Protocol)
  • Routing (RIP, EIGRP, OSPF, BGP, etc.)
  • IPsec (not required but recommended)

Cisco DMVPN Components

Main DMVPN Components

Dynamic VPN

  • Multipoint GRE and Point to Point GRE

  • NHRP (Next Hop Resolution Protocol)

  • Routing (RIP, EIGRP, OSPF, BGP, etc.)

  • IPsec (not required but recommended)

1st Lab Guide: Displaying the DMVPN configuration

DMVPN Network

The following screenshot is from a DMVPN network using Cisco modeling labs. We have R1 as the hub, R2 and R3 as the spokes. The command: show DMVPN displays that we have two spokes routers. Notice the “D” attribute. This means the spokes have been learned dynamically, which is the essence of DMVPN.

The spokes are learned with a process called the Next Hop Resolution Protocol. As this is a nonbroadcast multiaccess network, we must use a protocol other than the Address Resolution Protocol (ARP).

Note:

  1. As you can see, with tunnel configuration for one of the spokes, we have a static mapping for the hub with the command IP nhrp NHS 192.168.100.1.  We also have point-to-point GRE tunnels in the spokes with the command: tunnel destination 172.17.11.2.
  2. Therefore, we are running DMVPN Phase 1. DMVPN phase 3 will have mGRE. More on this later.
DMVPN configuration
Diagram: DMVPN Configuration.

Key DMVPN components include:

●   Multipoint GRE (mGRE) tunnel interface: Allows a single GRE interface to support multiple IPsec tunnels, simplifying the size and complexity of the configuration. Standard point-to-point GRE tunnels are used in the earlier versions or phases of DMVPN.

●   Dynamic discovery of IPsec tunnel endpoints and crypto profiles: Eliminates the need to configure static crypto maps defining every pair of IPsec peers, further simplifying the configuration.

●   NHRP: Allows spokes to be deployed with dynamically assigned public IP addresses (i.e., behind an ISP’s router). The hub maintains an NHRP database of the public interface addresses of each spoke. Each spoke registers its actual address when it boots; when it needs to build direct tunnels with other spokes, it queries the NHRP database for real addresses of the destination spokes

DMVPN Explained
Diagram: DMVPN explained. Source is TechTarget

DMVPN Explained

Overlay Networking

A Cisco DMVPN network consists of many overlay virtual networks. Such a virtual network is called an overlay network because it depends on an underlying transport called the underlay network. The underlay network forwards traffic flowing through the overlay network. With the use of protocol analyzers, the underlay network is aware of the existence of the overlay. However, left to its defaults, the underlay network does not fully see the overlay network.

We will have routers at the company’s sites that are considered the endpoints of the tunnel that forms the overlay network. So, we could have a WAN edge router or Cisco ASA configured for DMVPN.  Then, for the underlay that is likely out of your control, have an array of service provider equipment such as routers, switches, firewalls, and load balancers that make up the underlay.

The following diagram displays the different overlay solutions. VXLAN is expected in the data center, while GRE is used across the WAN. DMVPN uses GRE.

What is VXLAN
Diagram: Virtual overlay solutions.

2nd Lab Guide: VXLAN overlay

Overlay Networking

While DMVPN does not run VXLAN as the overlay protocol, viewing for background information and reference is helpful. VXLAN is a network overlay technology that provides a scalable and flexible solution for creating virtualized networks.

It enables the creation of logical Layer 2 networks over an existing Layer 3 infrastructure, allowing organizations to extend their networks across data centers and virtualized environments. In the following example, we create a Layer 2 overlay over a Layer 3 core. A significant difference between DMVPN’s use of GRE as the overlay and the use of VXLAN is the VNI.

Note:

  1. One critical component of VXLAN is the Virtual Network Identifier (VNI). In this blog post, we will explore the details of VXLAN VNI and its significance in modern network architectures.
  2. VNI is a 24-bit identifier that uniquely identifies a VXLAN network. It allows multiple VXLAN networks to coexist over the same physical network infrastructure. Each VNI represents a separate Layer 2 network, enabling the isolation and segmentation of traffic between different virtual networks.

Below, you can see the VNI used and the peers that have been created. VXLAN also works in multicast mode.

Overlay networking
Diagram: Overlay Networking with VXLAN

DMVPN Overlay Networking

Creating an overlay network


  • To create an overlay network, one needs a tunneling technique.Multipoint GRE and Point to Point GRE

  • GRE tunnel is the most widely used for external connectivity

  • VXLAN is for internal to the data center.

  • GRE tunnel support IP-based network, works by inserting IP and GRE header on top of the original protocol packet.

DMVPN: Creating an overlay network

The overlay network does not magically appear. To create one, we need a tunneling technique. Many tunneling technologies can be used to form the overlay network. The Generic Routing Encapsulation (GRE) tunnel is the most widely used external connectivity, while VXLAN is used for internal connectivity to the data center.

And the one that DMVPN adopts. A GRE tunnel can support tunneling for various protocols over an IP-based network. It works by inserting an IP and GRE header on top of the original protocol packet, creating a new GRE/IP packet. 

GRE over IPsec

The resulting GRE/IP packet uses a source/destination pair routable over the underlying infrastructure. The GRE/IP header is the outer header, and the original protocol header is the inner header.

♦ Is GRE over IPsec a tunneling protocol? 

GRE is a tunneling protocol that transports multicast, broadcast, and non-IP packets like IPX. IPSec is an encryption protocol. IPSec can only transport unicast packets, not multicast & broadcast. Hence, we wrap it GRE first and then into IPSec, which is called GRE over IPSec.

3rd Lab Guide: Displaying the DMVPN configuration

DMVPN Configuration

We are using a different DMVPN lab setup than before. R11 is the hub of the DMVPN network, and we only have one spoke of R12. In the DMVPN configuration, the tunnel interface has an “encapsulation tunnel.” This is the overlay network, and we are using GRE.

We are currently using the standard point-to-point GRE and not multipoint GRE. We know this as we have explicitly set the tunnel destination with the command tunnel destination 172.16.31.2.  This is fine for a small network of a few spokes. However, for the more extensive network, we need to use mGRE. And take full advantage of the dynamic nature of DMVPN.

Note:

  1. As for the routing protocols, we run EIGRP over the tunnel ( GRE ) interface. So, we only have one EIGRP neighbor, so we don’t need to worry about the split horizon. Before we move on, one key point is that running a traceroute from R11 to R12 only shows one hop.
  2. This is because the TTL is also carried in the GRE. So, no matter how many devices are in the path ( underlay network ) between R11 and R12, either physical or virtual, it will always show as one hop due to the overlay network, i.e., GRE.
DMVPN configuration
Diagram: DMVPN Configuration

Multipoint GRE. What is mGRE? 

An alternative to configuring multiple point-to-point GRE tunnels is to use multipoint GRE tunnels to provide the connectivity desired. Multipoint GRE (mGRE) tunnels are similar in construction to point-to-point GRE tunnels except for the tunnel destination command. However, instead of declaring a static destination, no destination is declared, and instead, the tunnel mode gre multipoint command is issued.

How does one remote site know what destination to set for the GRE/IP packet created by the tunnel interface? The easy answer is that it can’t on its own. The site can only glean the destination address with the help of an additional protocol. The next component used to create a DMVPN network is the Next Hop Resolution Protocol (NHRP). 

Essentially, mGRE features a single GRE interface on each router, allowing multiple destinations. This interface secures multiple IPsec tunnels and reduces the overall scope of the DMVPN configuration. However, if two branch routers need to tunnel traffic, mGRE and point-to-point GRE may not know which IP addresses to use.

The Next Hop Resolution Protocol (NHRP) is used to solve this issue. The following diagram depicts the functionality of mGRE in DMVPN technology.

what is mgre
Diagram: What is mGRE? Source is Stucknactive

Next Hop Resolution Protocol (NHRP)

The Next Hop Resolution Protocol (NHRP) is a networking protocol designed to facilitate efficient and reliable communication between two nodes on a network. It does this by providing a way for one node to discover the IP address of another node on the same network.

The primary role of NHRP is to allow a node to resolve the IP address of another node that is not directly connected to the same network. This is done by querying an NHRP server, which contains a mapping of all the nodes on the network. When a node requests the NHRP server, it will return the IP address of the destination node.

NHRP was initially designed to allow routers connected to non-broadcast multiple-access (NBMA) networks to discover the proper next-hop mappings to communicate. It is specified in RFC 2332. NBMA networks faced a similar issue as mGRE tunnels. 

Cisco DMVPN
Diagram: Cisco DMVPN and NHRP. The source is network direction.

The NHRP can deploy spokes with assigned IP addresses, which can be connected from the central DMVPN hub. One branch router requires this protocol to find the public IP address of the second branch router. NHRP uses a “server-client” model, where one router functions as the NHRP server while the other routers are the NHRP clients. In the multipoint GRE/DMVPN topology, the hub router is the NHRP server, and all other routers are the spokes. 

Each client registers with the server and reports its public IP address, which the server tracks in its cache. Then, through a process that involves registration and resolution requests from the client routers and resolution replies from the server router, traffic is enabled between various routers in the DMVPN.

4th Lab Guide: Displaying the DMVPN configuration

NHC and NHS Design

The following DMVPN configuration shows a couple of new topics. DMVPN works with an NHS and NHC design; the hub is the HHS. You can see this explicitly configured on the spokes, and this configuration needs to be on the spokes rather than the hubs.

The hub configuration is meant to be more dynamic. Also, if you recall, we are running EIGRP over the GRE tunnel. Two important points here. Firstly, we must consider a split-horizon as we have two spokes. Secondly, we need to use the “multicast” command.

Note:

  1. This is because EIGRP uses multicast HELLO messages to form neighbor relationships. If we had BGP running over the tunnel interface and not EIGRP, we would not need the multicast keywords. As BGP does not use multicast. The full command: IP nhrp nhs 192.168.100.11 nmba 172.16.11.1 multicast.
  2. On the spoke, we are telling the router that R11 is the NHS and to map its tunnel interface of 192.168.100.11 to the address 172.16.11.1 and to allow multicast traffic, or better explained, we are creating a new multicast mapping table.

DMVPN configuration

5th Lab Guide: DMVPN over IPsec

Securing DMVPN

In the following screenshot, we have DMVPN operating over IPsec. So, I have connected the hub and two spokes into an unmanaged switch to simulate the WAN environment. A WAN and DMVPN do not have encryption by default. However, since you probably use DMVPN with the Internet as the underlying network, it might be wise to encrypt your tunnels.

In this network, we are running RIP v2 as the routing protocol. Remember that you must turn off the split horizon at the hub site. IPsec has phases 1 and 2 (don’t confuse them with the DMVPN phases). Firstly, we need an ISAKMP policy that matches all our routers. Then, for Phase 2, we require a transform set on each router that tells the router what encryption/hashing to use and if we want tunnel or transport mode.

Note:

  1. I used ESP with AES as the encryption algorithm for this configuration and SHA for hashing. The mode is essential; since we are using GRE, we have already used tunnels as a transport mode. If you use tunnel mode, we will have even more overhead, which is unnecessary.
  2. The primary test here was to run a ping between the spokes. Since the ping works behind the scenes, our two spoke routers will establish an IPsec tunnel. You can see the security association below:
DMVPN over IPsec
Diagram: DMVPN over IPsec

IPsec Tunnels

An IPsec tunnel is a secure connection between two or more devices over an untrusted network using a set of cryptographic security protocols. The most common type of IPsec tunnel is the site-to-site tunnel, which connects two sites or networks. It allows two remote sites to communicate securely and exchange traffic between them. Another type of IPsec tunnel is the remote-access tunnel, which allows a remote user to connect to the corporate network securely.

When setting up an IPsec tunnel, several parameters, such as authentication method, encryption algorithm, and tunnel mode, must be configured. Depending on the organization’s needs, additional security protocols, such as Internet Key Exchange (IKE), can also be used for further authentication and encryption.

IPsec VPN
Diagram: IPsec VPN. Source Wikimedia.

IPsec Tunnel Endpoint Discovery 

Tunnel Endpoint Discovery (TED) allows routers to discover IPsec endpoints automatically so that static crypto maps must not be configured between individual IPsec tunnel endpoints. In addition, TED allows endpoints or peers to dynamically and proactively initiate the negotiation of IPsec tunnels to discover unknown peers.

These remote peers do not need to have TED configured to be discovered by inbound TED probes. So, while configuring TED, VPN devices that receive TED probes on interfaces — that are not configured for TED — can negotiate a dynamically initiated tunnel using TED.

DMVPN Checkpoint 

Main DMVPN Points To Consisder

  • Dynamic Multipoint VPN (DMVPN) technology is used for scaling IPsec VPN networks.

  • The DMVPN solution consists of a combination of existing technologies.

  • The overlay network does not magically appear. To create an overlay network, we need a tunneling technique. 

  • Once the virtual tunnel is fully functional, the routers need a way to direct traffic through their tunnels. Dynamic routing protocols are excellent choices for this.

  • mGRE features a single GRE interface on each router with the possibility of multiple destinations.

  • The Next Hop Resolution Protocol (NHRP) is a networking protocol designed to facilitate efficient and reliable communication between two nodes on a network.

  • An IPsec tunnel is a secure connection between two or more devices over an untrusted network using a set of cryptographic security protocols. DMVPN is not secure by default.

Continue Reading


DMVPN and Routing protocols 

Routing protocols enable the DMVPN to find routes between different endpoints efficiently and effectively. Therefore, choosing the right routing protocol is essential to building a scalable and stable DMVPN. One option is to use Open Shortest Path First (OSPF) as the interior routing protocol. However, OSPF is best suited for small-scale DMVPN deployments. 

The Enhanced Interior Gateway Routing Protocol (EIGRP) or Border Gateway Protocol (BGP) is more suitable for large-scale implementations. EIGRP is not restricted by the topology limitations of a link-state protocol and is easier to deploy and scale in a DMVPN topology. BGP can scale to many peers and routes, and it puts less strain on the routers compared to other routing protocols

DMVPN supports various routing protocols that enable efficient communication between network devices. In this section, we will explore three popular DMVPN routing protocols: Enhanced Interior Gateway Routing Protocol (EIGRP), Open Shortest Path First (OSPF), and Border Gateway Protocol (BGP). We will examine their characteristics, advantages, and use cases, allowing network administrators to make informed decisions when implementing DMVPN.

EIGRP: The Dynamic Routing Powerhouse

EIGRP is a distance vector routing protocol widely used in DMVPN deployments. This section will provide an in-depth look at EIGRP, discussing its features such as fast convergence, load balancing, and scalability. Furthermore, we will highlight best practices for configuring EIGRP in a DMVPN environment, optimizing network performance and reliability.

OSPF: Scalable and Flexible Routing

OSPF is a link-state routing protocol that offers excellent scalability and flexibility in DMVPN networks. This section will explore OSPF’s key attributes, including its hierarchical design, area types, and route summarization capabilities. We will also discuss considerations for deploying OSPF in a DMVPN environment, ensuring seamless connectivity and effective network management.

BGP: Extending DMVPN to the Internet

BGP, a path vector routing protocol, connects DMVPN networks to the global Internet. This section will focus on BGP’s unique characteristics, such as its autonomous system (AS) concept, policy-based routing, and route reflectors. We will also address the challenges and best practices of integrating BGP into DMVPN architectures.

6th Lab Guide: DMVPN Phase 1 and OSPF

DMVPN Routing

OSPF is not the best solution for DMVPN. Because it’s a link-state protocol, each spoke router must have the complete LSDB for the DMVPN area. Since we use a single subnet on the multipoint GRE interfaces, all spoke routers must be in the same area.

This is no problem with a few routers, but it doesn’t scale well when you have dozens or hundreds. Most spoke routers are probably low-end devices at branch offices that don’t like all the LSA flooding that OSPF might do within the area. One way to reduce the number of prefixes in the DMVPN network is to use a stub or total stub area.

Note:

  1. The example below shows we are running OSPF between the Hub and the two spokes. OSPF network type can be viewed on the hub along with the status of DMVPN. Please take note of the next hop on the Spoke router when I do a show IP route OSPF.
  2. Each router has learned the networks on the different loopback interfaces. The next hop value is preserved when we use the broadcast network type.

You have seen all the different OSPF Broadcast network types on DMVPN phase 1. As you can see, some stuff is in the routing tables. All traffic goes through the hub, so our spoke routers don’t need to know everything. Unfortunately, it’s impossible to summarize within the area. However, we can reduce the number of routes by changing the DMVPN area into a stub or total stub area.

Unlike the broadcast network type, point-to-point and point-to-multipoint network types do not preserve the spokes’ next-hop IP addresses.

7th Lab Guide: DMVPN Phase 2 with OSPF

DMVPN Routing

In the following example, we have DMVPN Phase 2 running with OSPF. We are using the Broadcast network type. However, the following OSPF network types are potentials for DMVPN phase 2.

  • point-to-point
  • broadcast
  • non-broadcast
  • point-to-multipoint
  • point-to-multipoint non-broadcast

Below, all routers have learned the networks on each other’s loopback interfaces. Look closely at the next hop IP addresses for the 2.2.2.2/32 and 3.3.3.3/32 entries. This looks good; these are the IP addresses of the spoke routers. You can also see that 1.1.1.1/32 is an inter-area route. This is good; we can summarize networks “behind” the hub towards the spoke routers if we want to.

When running OSPF for DMVPN phase 2, you only have two choices if you want direct spoke-to-spoke communication: broadcast and non-broadcast. Let me give you an overview:

  • Point-to-point: This will not work since we use multipoint GRE interfaces.
  • Broadcast: This network type is your best choice. We are using it in the example above. The automatic neighbor discovery and correct next-hop addresses. Make sure that the spoke router can’t become DR or BDR. Here, we can use the priority commands and set them to 0 on the spokes.
  • Non-broadcast: similar to broadcast, but you have to configure static neighbors.
  • Point-to-multipoint: Don’t use this for DMVPN phase 2 since the hub changes the next hop address; you won’t have direct spoke-to-spoke communication.
  • Point-to-multipoint non-broadcast: same story as point-to-multipoint, but you must also configure static neighbors.

DMVPN Deployment Scenarios: 

Cisco DMVPN can be deployed in two ways:

  1. Hub-and-spoke deployment model
  2. Spoke-to-spoke deployment model

Hub-and-spoke deployment model: In this traditional topology, remote sites, which are the spokes, are aggregated into a headend VPN device. The headend VPN location would be at the corporate headquarters, known as the hub. 

Traffic from any remote site to other remote sites would need to pass through the headend device. Cisco DMVPN supports dynamic routing, QoS, and IP Multicast while significantly reducing the configuration effort. 

Spoke-to-spoke deployment model: Cisco DMVPN allows the creation of a full-mesh VPN, in which traditional hub-and-spoke connectivity is supplemented by dynamically created IPsec tunnels directly between the spokes. 

With direct spoke-to-spoke tunnels, traffic between remote sites does not need to traverse the hub; this eliminates additional delays and conserves WAN bandwidth while improving performance. 

Spoke-to-spoke capability is supported in a single-hub or multi-hub environment. Multihub deployments provide increased spoke-to-spoke resiliency and redundancy.  

DMVPN Designs

The word phase is almost always connected to discussions on DMVPN design. DMVPN phase refers to the version of DMVPN implemented in a DMVPN design. As mentioned above, we can have two deployment models, each of which can be mapped to a DMVPN Phase.

Cisco DMVPN as a solution was rolled out in different stages as the explanation became more widely adopted to address performance issues and additional improvised features. There are three main phases for DMVPN:

  • Phase 1 – Hub-and-spoke
  • Phase 2 – Spoke-initiated spoke-to-spoke tunnels
  • Phase 3 – Hub-initiated spoke-to-spoke tunnels
What is DMVPN
Diagram: What is DMVPN? Source is Lira

The differences between the DMVPN phases are related to routing efficiency and the ability to create spoke-to-spoke tunnels. We started with DMVPN Phase 1, which only had a hub to spoke. This needed more scalability as we could not have direct spoke-to-spoke communication. Instead, the spokes could communicate with one another but were required to traverse the hub.

Then, we went to DMVPN Phase 2 to support spoke-to-spoke with dynamic tunnels. These tunnels were initially brought up by passing traffic via the hub. Later, Cisco developed DMVPN Phase 3, which optimized how spoke-to-spoke commutation happens and the tunnel build-up.

Dynamic multipoint virtual private networks began simply as what is best described as hub-and-spoke topologies. The primary tool for creating these VPNs combines Multipoint Generic Routing Encapsulation (mGRE) connections employed on the hub with traditional Point-to-Point (P2P) GRE tunnels on the spoke devices.

In this initial deployment methodology, known as a Phase 1 DMVPN, the spokes can only join the hub and communicate with one another through the hub. This phase does not use spoke-to-spoke tunnels. Instead, the spokes are configured for point-to-point GRE to the hub and register their logical IP with the non-broadcast multi-access (NBMA) address on the next hop server (NHS) hub.

It is essential to keep in mind that there is a total of three phases, and each one can influence the following:

  1. Spoke-to-spoke traffic patterns
  2. Routing protocol design
  3. Scalability

DMVPN Design Options

The disadvantage of a single hub router is that it’s a single point of failure. Once your hub router fails, the entire DMVPN network is gone.

We need another hub router to add redundancy to our DMVPN network. There are two options for this:

  1. Dual hub – Single Cloud
  2. Dual hub – Dual Cloud

With the single cloud option, we use a single DMVPN network but add a second hub. The spoke routers will use only one multipoint GRE interface, and we configure the second hub as a next-hop server. The dual cloud option also has two hubs, but we will use two DMVPN networks, meaning all spoke routers will get a second multipoint GRE interface.

Understanding DMVPN Dual Hub Single Cloud:

DMVPN dual hub single cloud is a network architecture that provides redundancy and high availability by utilizing two hub devices connected to a single cloud. The cloud can be an internet-based infrastructure or a private WAN. This configuration ensures the network remains operational even if one hub fails, as the other hub takes over the traffic routing responsibilities.

Benefits of DMVPN Dual Hub Single Cloud:

1. Redundancy: With dual hubs, organizations can ensure network availability even during hub device failures. This redundancy minimizes downtime and maximizes productivity.

2. Load Balancing: DMVPN dual hub single cloud allows for efficient load balancing between the two hubs. Traffic can be distributed evenly, optimizing bandwidth utilization and enhancing network performance.

3. Scalability: The architecture is highly scalable, allowing organizations to easily add new sites without reconfiguring the entire network. New sites can be connected to either hub, providing flexibility and ease of expansion.

4. Simplified Management: DMVPN dual hub single cloud simplifies network management by centralizing control and reducing the complexity of VPN configurations. Changes and updates can be made at the hub level, ensuring consistent policies across all connected sites.

The disadvantage is that we have limited control over routing. Since we use a single multipoint GRE interface, making the spoke routers prefer one hub over another is challenging.

8th Lab Guide: DMVPN Dual Hub Single Cloud

DMVPN Advanced Configuration

Below is a DMVPN network with two hubs and two spoke routers. Hub1 will be the primary hub, and hub2 will be the secondary hub. We use a single DMVPN network; each router only has one multipoint GRE interface. On top of that, we have R1 on the leading site where we use the 10.10.10.0/24 subnet. Behind R1, we have a loopback interface with IP address 1.1.1.1/32.

Note:

  1. The two hub routers and spoke routers are connected to the Internet. Usually, you would connect the two hub routers to different ISPs. To keep it simple, I combined all routers into the 192.168.1.0/24 subnet, represented as an unmanaged switch in the lab below.
  2. Each spoke router has a loopback interface with an IP address. The DMVPN network will use subnet 172.16.1.0/24, where hub1 will be the primary hub. Spoke routers will register themselves with both hub routers.
Dual Hub Single Cloud
Diagram: Dual Hub Single Cloud

Summary of DMVPN Phases

Phase 1—Hub-to-Spoke Designs: Phase 1 was the first design introduced for hub-to-spoke implementation, where spoke-to-spoke traffic would traverse via the hub. Phase 1 also introduced daisy chaining of identical hubs for scaling the network, thereby providing Server Load Balancing (SLB) capability to increase the CPU power.

Phase 2—Spoke-to-Spoke Designs: Phase 2 design introduced the ability for dynamic spoke-to-spoke tunnels without traffic going through the hub, intersite communication bypassing the hub, thereby providing greater scalability and better traffic control.

In Phase 2 network design, each DMVPN network is independent of other DMVPN networks, causing spoke-to-spoke traffic from different regions to traverse the regional hubs without going through the central hub.

Phase 3—Hierarchical (Tree-Based) Designs: Phase 3 extended Phase 2 design with the capability to establish dynamic and direct spoke-to-spoke tunnels from different DMVPN networks across multiple regions. In Phase 3, all regional DMVPN networks are bound to form a single hierarchical (tree-based) DMVPN network, including the central hubs.

As a result, spoke-to-spoke traffic from different regions can establish direct tunnels with each other, thereby bypassing both the regional and main hubs.

DMVPN network
Diagram: DMVPN network and phases explained. Source is blog

DMVPN Architecture

Design recommendation


  • To create an overlay network, one needs a tunneling technique.Multipoint GRE and Point to Point GRE

  • Dynamic routing protocols are typically required in all but the smallest deployments or wherever static routing is not manageable or optimal.

  • QoS: Mandatory to ensure performance and quality of voice, video, and real-time data applications.

DMVPN Design recommendation

Which deployment model can you use? The 80:20 traffic rule can be used to determine which model to use:

  1. If 80 percent or more of the traffic from the spokes is directed into the hub network itself, deploy the hub-and-spoke model.
  2. Consider the spoke-to-spoke model if more than 20 percent of the traffic is meant for other spokes.

The hub-and-spoke model is usually preferred for networks with a high volume of IP Multicast traffic.

Architecture

Medium-sized and large-scale site-to-site VPN deployments require support for advanced IP network services such as:

● IP Multicast: Required for efficient and scalable one-to-many (i.e., Internet broadcast) and many-to-many (i.e., conferencing) communications and commonly needed by voice, video, and specific data applications

● Dynamic routing protocols: Typically required in all but the smallest deployments or wherever static routing is not manageable or optimal

● QoS: Mandatory to ensure performance and quality of voice, video, and real-time data applications

Traditionally, supporting these services required tunneling IPsec inside protocols such as Generic Route Encapsulation (GRE), which introduced an overlay network, making it complex to set up and manage and limiting the solution’s scalability.

Indeed, traditional IPsec only supports IP Unicast, making deploying applications that involve one-to-many and many-to-many communications inefficient. Cisco DMVPN combines GRE tunneling and IPsec encryption with Next-Hop Resolution Protocol (NHRP) routing to meet these requirements while reducing the administrative burden. 

How DMVPN Works

How DMVPN Works


  •  Each spoke establishes a permanent tunnel to the hub. IPsec is optional.

  • Each spoke registers its actual address as a client to the NHRP server on the hub

  • When a spoke requires that packets be sent to a destination subnet on another spoke, it queries the NHRP server for the real (outside) addresses of other spoke.

  • After the originating spoke learns the peer address of the target spoke, it initiates a dynamic IPsec tunnel to the target spoke.

  • The spoke-to-spoke tunnels are established on demand whenever traffic is sent between the spokes.

  • DMVPN Operations.

How DMVPN Works

DMVPN builds a dynamic tunnel overlay network.

• Initially, each spoke establishes a permanent IPsec tunnel to the hub. (At this stage, spokes do not establish tunnels with other spokes within the network.) The hub address should be static and known by all of the spokes.

• Each spoke registers its actual address as a client to the NHRP server on the hub. The NHRP server maintains an NHRP database of the public interface addresses for each spoke.

• When a spoke requires that packets be sent to a destination (private) subnet on another spoke, it queries the NHRP server for the real (outside) addresses of the other spoke’s destination to build direct tunnels.

• The NHRP server looks up the NHRP database for the corresponding destination spoke and replies with the real address for the target router. NHRP prevents dynamic routing protocols from discovering the route to the correct spoke. (Dynamic routing adjacencies are established only from spoke to the hub.)

• After the originating spoke learns the peer address of the target spoke, it initiates a dynamic IPsec tunnel to the target spoke.

• Integrating the multipoint GRE (mGRE) interface, NHRP, and IPsec establishes a direct dynamic spoke-to-spoke tunnel over the DMVPN network.

The spoke-to-spoke tunnels are established on demand whenever traffic is sent between the spokes. After that, packets can bypass the hub and use the spoke-to-spoke tunnel directly. 

Feature Design of Dynamic Multipoint VPN 

The Dynamic Multipoint VPN (DMVPN) feature combines GRE tunnels, IPsec encryption, and NHRP routing to provide users with ease of configuration via crypto profiles—which override the requirement for defining static crypto maps—and dynamic discovery of tunnel endpoints. 

This feature relies on the following two Cisco-enhanced standard technologies: 

  • NHRP is a client-server protocol where the hub is the server and the spokes are the clients. The hub maintains an NHRP database of each spoke’s public interface addresses. Each spoke registers its real address when it boots and queries the NHRP database for the real addresses of the destination spokes to build direct tunnels. 
  • mGRE Tunnel Interface –Allows a single GRE interface to support multiple IPsec tunnels and simplifies the size and complexity of the configuration.
  • Each spoke has a permanent IPsec tunnel to the hub, not to the other spokes within the network. Each spoke registers as a client of the NHRP server. 
  • When a spoke needs to send a packet to a destination (private) subnet on another spoke, it queries the NHRP server for the real (outside) address of the destination (target) spoke. 
  • After the originating spoke “learns” the peer address of the target spoke, a dynamic IPsec tunnel can be initiated into the target spoke. 
  • The spoke-to-spoke tunnel is built over the multipoint GRE interface. 
  • The spoke-to-spoke links are established on demand whenever there is traffic between the spokes. After that, packets can bypass the hub and use the spoke-to-spoke tunnel.
Cisco DMVPN
Diagram: Cisco DMVPN features. The source is Cisco.

Cisco DMVPN Solution Architecture

DMVPN allows IPsec VPN networks to scale hub-to-spoke and spoke-to-spoke designs better, optimizing performance and reducing communication latency between sites.

DMVPN offers a wide range of benefits, including the following:

• The capability to build dynamic hub-to-spoke and spoke-to-spoke IPsec tunnels

• Optimized network performance

• Reduced latency for real-time applications

• Reduced router configuration on the hub that provides the capability to dynamically add multiple spoke tunnels without touching the hub configuration

• Automatic triggering of IPsec encryption by GRE tunnel source and destination, assuring zero packet loss

• Support for spoke routers with dynamic physical interface IP addresses (for example, DSL and cable connections)

• The capability to establish dynamic and direct spoke-to-spoke IPsec tunnels for communication between sites without having the traffic go through the hub; that is, intersite communication bypassing the hub

• Support for dynamic routing protocols running over the DMVPN tunnels

• Support for multicast traffic from hub to spokes

• Support for VPN Routing and Forwarding (VRF) integration extended in multiprotocol label switching (MPLS) networks

• Self-healing capability maximizing VPN tunnel uptime by rerouting around network link failures

• Load-balancing capability offering increased performance by transparently terminating VPN connections to multiple headend VPN devices

Network availability over a secure channel is critical in designing scalable IPsec VPN solutions prepared with networks becoming geographically distributed. DMVPN solution architecture is by far the most effective and scalable solution available.

Summary: DMVPM

One technology that has gained significant attention and revolutionized the way networks are connected is DMVPN (Dynamic Multipoint Virtual Private Network). In this blog post, we delved into the depths of DMVPN, exploring its architecture, benefits, and use cases.

Section 1: Understanding DMVPN

DMVPN, at its core, is a scalable and efficient solution for providing secure and dynamic connectivity between multiple sites over a public network infrastructure. It combines the best features of traditional VPNs and multipoint GRE tunnels, resulting in a flexible and cost-effective network solution.

Section 2: The Architecture of DMVPN

The architecture of DMVPN involves three main components: the hub router, the spoke routers, and the underlying routing protocol. The hub router acts as a central point for the network, while the spoke routers establish secure tunnels with the hub. These tunnels are dynamically built using multipoint GRE, allowing efficient data transmission.

Section 3: Benefits of DMVPN

3.1 Enhanced Scalability: DMVPN provides a scalable solution, allowing for easy addition or removal of spokes without complex configurations. This flexibility is particularly useful in dynamic network environments.

3.2 Cost Efficiency: Using existing public network infrastructure, DMVPN eliminates the need for costly dedicated lines or leased circuits. This significantly reduces operational expenses and makes it an attractive option for organizations of all sizes.

3.3 Simplified Management: With DMVPN, network administrators can centrally manage the network infrastructure through the hub router. This centralized control simplifies configuration, monitoring, and troubleshooting tasks.

Section 4: Use Cases of DMVPN

4.1 Branch Office Connectivity: DMVPN is ideal for connecting branch offices to a central headquarters. It provides secure and reliable communication while minimizing the complexity and cost associated with traditional WAN solutions.

4.2 Mobile or Remote Workforce: DMVPN offers a secure and efficient solution for connecting remote employees to the corporate network in today’s mobile-centric work environment. Whether it’s sales representatives or telecommuters, DMVPN ensures seamless connectivity regardless of the location.

Conclusion:

DMVPN has emerged as a game-changer in the world of network connectivity. Its scalable architecture, cost efficiency, and simplified management make it an attractive option for organizations seeking to enhance their network infrastructure. Whether connecting branch offices or enabling a mobile workforce, DMVPN provides a robust and secure solution. Embracing the power of DMVPN can revolutionize network connectivity, opening doors to a more connected and efficient future.

eBOOK on SASE Capabilities

eBOOK – SASE Capabilities

In the following ebook, we will address the key points:

  1. Challenging Landscape
  2. The rise of SASE based on new requirements
  3. SASE definition
  4. Core SASE capabilities
  5. Final recommendations

 

 

Preliminary Information: Useful Links to Relevant Content

For pre-information, you may find the following links useful:

 

Secure Access Service Edge (SASE) is a service designed to provide secure access to cloud applications, data, and infrastructure from anywhere. It allows organizations to securely deploy applications and services from the cloud while managing users and devices from a single platform. As a result, SASE simplifies the IT landscape and reduces the cost and complexity of managing security for cloud applications and services.

SASE provides a unified security platform allowing organizations to connect users and applications securely without managing multiple security solutions. It offers secure access to cloud applications, data, and infrastructure with a single set of policies, regardless of the user’s physical location. It also enables organizations to monitor and control all user activities with the same set of security policies.

SASE also helps organizations reduce the risk of data breaches and malicious actors by providing visibility into user activity and access. In addition, it offers end-to-end encryption, secure authentication, and secure access control. It also includes threat detection, advanced analytics, and data loss prevention.

SASE allows organizations to scale their security infrastructure quickly and easily, providing them with a unified security platform that can be used to connect users and applications from anywhere securely. With SASE, organizations can quickly and securely deploy applications and services from the cloud while managing users and devices from a single platform.

 

 

 

 

POZNAN, POL - APR 15, 2021: Laptop computer displaying logo of OpenShift, a family of containerization software products developed by Red Hat

OpenShift Networking

OpenShift Networking

OpenShift, developed by Red Hat, is a leading container platform that enables organizations to streamline their application development and deployment processes. With its robust networking capabilities, OpenShift provides a secure and scalable environment for running containerized applications. This blog post will explore the critical aspects of OpenShift networking and how it can benefit your organization.

OpenShift networking is built on top of Kubernetes networking and extends its capabilities to provide a flexible and scalable networking solution for containerized applications. It offers various networking options to meet the diverse needs of different organizations.

Load balancing and service discovery are essential aspects of Openshift networking. In this section, we will explore how Openshift handles load balancing across pods using services. We will discuss the various load balancing algorithms available and highlight the importance of service discovery in ensuring seamless communication between microservices within an Openshift cluster.

Openshift offers different networking models to suit diverse deployment scenarios. We will explore the three main models: Overlay Networking, Host Networking, and VxLAN Networking. Each model has its advantages and considerations, and we'll highlight the use cases where they shine.

Openshift provides several advanced networking features that enhance performance, security, and flexibility. We'll dive into topics like Network Policies, Service Mesh, Ingress Controllers, and Load Balancing. Understanding and utilizing these features will empower you to optimize your Openshift networking environment.

Highlights:OpenShift Networking

Overview – OpenShift Networking

OpenShift networking provides a robust and scalable network infrastructure for applications running on the platform. It allows containers and pods to communicate with each other and external systems and services. The networking model in OpenShift is based on the Kubernetes networking model, providing a standardized and flexible approach.

Software-Defined Networking (SDN) is a crucial component of OpenShift Networking, enabling dynamic, programmatically efficient network configuration. SDN abstracts the traditional networking hardware, providing a flexible network fabric that can adapt to the needs of your applications. SDN operates within OpenShift, enabling improved scalability, enhanced security, and simplified network management.

Key Initial Considerations:

A. OpenShift Container Platform

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 foundation of the OpenShift Container Platform is based on Kubernetes and, therefore, shares some of the same networking technology along with some enhancements.

B. Kubernetes: Orchestration Layer

Kubernetes is the leading container orchestration, and OpenShift is derived from containers, with Kubernetes as the orchestration layer. All of these elements lie upon an SDN layer that glues everything together by providing an abstraction layer. SDN creates the cluster-wide network. The glue that connects all the dots is the overlay network that operates over an underlay network. 

C. OpenShift Networking & Plugins

When it comes to container orchestration, networking plays a pivotal role in ensuring connectivity and communication between containers, pods, and services. Openshift Networking provides a robust framework that enables efficient and secure networking within the Openshift cluster. By leveraging various networking plugins and technologies, it facilitates seamless communication between applications and allows load balancing, service discovery, and more.

The Architecture of OpenShift Networking

OpenShift’s networking model is built upon the foundation of Kubernetes, but it takes things a step further with its own enhancements bringing better networking and security capabilities than the default Kubernetes model. The architecture consists of several key components, including the OpenShift SDN (Software Defined Networking), network policies, and service mesh capabilities.

The OpenShift SDN abstracts the underlying network infrastructure, allowing developers to focus on application logic rather than network configurations. This abstraction enables greater flexibility and simplifies the deployment process.

**Network Policies: Securing Your Cluster**

One of the standout features of OpenShift networking is the implementation of network policies. These policies allow administrators to define how pods communicate with each other and with the outside world. By leveraging network policies, teams can enforce security boundaries, ensuring that only authorized traffic is allowed to flow within the cluster. This is particularly crucial in multi-tenant environments where different teams might share the same OpenShift cluster but require isolated communication channels.

**Service Mesh: Enhancing Microservices Communication**

As organizations increasingly adopt microservices architectures, managing service-to-service communication becomes a challenge. OpenShift addresses this with its service mesh capabilities, primarily through the integration of Istio. A service mesh adds an additional layer of security, observability, and resilience to your microservices. It provides features like advanced traffic management, circuit breaking, and telemetry collection, all of which are essential for maintaining a healthy and efficient microservices ecosystem.

**Navigating Through OpenShift’s Route and Ingress**

Routing is another fundamental aspect of OpenShift networking. OpenShift’s routing layer allows developers to expose their applications to external users. This can be achieved through both routes and ingress resources. Routes are specific to OpenShift and provide a simple way to map external URLs to services within the cluster. On the other hand, ingress resources, which are part of Kubernetes, offer more advanced configurations and are particularly useful when you need to define complex routing rules.

Example Technology: Cloud Service Mesh

 

Key Considerations: OpenShift Networking

1. Network Namespace Isolation:
OpenShift networking leverages network namespaces to achieve isolation between different projects, or namespaces, on the platform. Each project has its virtual network, ensuring that containers and pods within a project can communicate securely while isolated from other projects.

2. Service Discovery and OpenShift Load Balancer:
OpenShift networking provides service discovery and load-balancing mechanisms to facilitate communication between various application components. Services act as stable endpoints, allowing containers and pods to connect to them using DNS or environmental variables. The built-in OpenShift load balancer ensures that traffic is distributed evenly across multiple instances of a service, improving scalability and reliability.

3. Ingress and Egress Network Policies:
OpenShift networking allows administrators to define ingress and egress network policies to control network traffic flow within the platform. Ingress policies specify rules for incoming traffic, allowing or denying access to specific services or pods. Egress policies, on the other hand, regulate outgoing traffic from pods, enabling administrators to restrict access to external systems or services.

4. Network Plugins and Providers:
OpenShift networking supports various network plugins and providers, allowing users to choose the networking solution that best fits their requirements. Some popular options include Open vSwitch (OVS), Flannel, Calico, and Multus. These plugins provide additional capabilities such as network isolation, advanced routing, and security features.

5. Network Monitoring and Troubleshooting:
OpenShift provides robust monitoring and troubleshooting tools to help administrators track network performance and resolve issues. The platform integrates with monitoring systems like Prometheus, allowing users to collect and analyze network metrics. Additionally, OpenShift provides logging and debugging features to aid in identifying and resolving network-related problems.

Example Technology: Prometheus Pull Approach 

Prometheus YAML file

OpenShift Networking Features

OpenShift Networking offers many features that empower developers and administrators to build and manage scalable applications. Some notable features include:

1. SDN Integration: Openshift seamlessly integrates with Software-Defined Networking (SDN) solutions, allowing for flexible network configurations and efficient traffic routing.

2. Multi-tenancy Support: With Openshift Networking, you can create isolated network zones, enabling multiple teams or projects to coexist within the same cluster while maintaining secure communication.

3. Service Load Balancing: Openshift Networking incorporates built-in load balancing capabilities, distributing incoming traffic across multiple instances of a service, thus ensuring high availability and optimal performance.

**Key Challenges: Traditional data center**

Several challenges with traditional data center networks prove they cannot support today’s applications, such as microservices and containers. Therefore, we need a new set of networking technologies built into OpenShift SDN to deal adequately with today’s landscape changes.

– Firstly, one of the main issues is that we have a tight coupling with all the networking and infrastructure components. With traditional data center networking, Layer 4 is coupled with the network topology at fixed network points and lacks the flexibility to support today’s containerized applications, which are more agile than traditional monolith applications.

– One of the main issues is that containers are short-lived and constantly spun down. Assets that support the application, such as IP addresses, firewalls, policies, and overlay networks that glue the connectivity, are continually recycled. These changes bring a lot of agility and business benefits, but there is an extensive comparison to a traditional network that is relatively static, where changes happen every few months.

OpenShift networking – Two layers

  1. In the case of OpenShift itself deployed in the virtual environment, the physical network equipment directly determines the underlying network topology. OpenShift does not control this level, which provides connectivity to OpenShift masters and nodes.
  2. OpenShift SDN plugin determines the virtual network topology. At this level, applications are connected, and external access is provided.

OpenShift uses an overlay network based on VXLAN to enable containers to communicate with each other. Layer 2 (Ethernet) frames can be transferred across Layer 3 (IP) networks using the Virtual Extensible Local Area Network (VXLAN) protocol.

Whether the communication is limited to pods within the same project or completely unrestricted depends on the SDN plugin being used. The network topology remains the same regardless of which plugin is used. OpenShift makes its internal SDN plugins available out-of-the-box and for integration with third-party SDN frameworks. 

Example Overlay Technology: VXLAN

VXLAN unicast mode

OpenShift Networking Plugins

### Understanding OpenShift Networking Plugins

OpenShift networking plugins are essential for managing network traffic within a cluster. They determine how pods communicate with each other and with external networks. The choice of networking plugin can significantly impact the performance and security of your applications. OpenShift supports several plugins, each with distinct features, allowing administrators to tailor their network configuration to specific needs.

### Popular Networking Plugins in OpenShift

1. **OVN-Kubernetes**: This plugin is widely used for its scalability and support for network policies. It provides a flexible, secure, and scalable network solution that is ideal for complex deployments. OVN-Kubernetes integrates seamlessly with Kubernetes, offering enhanced network isolation and simplified network management.

2. **Calico**: Known for its simplicity and efficiency, Calico provides high-performance network connectivity. It excels in environments that require fine-grained network policies and is particularly popular in microservices architectures. Calico’s support for IPv6 and integration with Kubernetes NetworkPolicy API makes it a versatile choice.

3. **Flannel**: A simpler alternative, Flannel is suitable for users who prioritize ease of setup and basic networking functionality. It uses a flat network model, which is straightforward to configure and manage, making it a good choice for smaller clusters or those in the early stages of development.

### Choosing the Right Plugin

Selecting the appropriate networking plugin for your OpenShift environment depends on several factors, including your organization’s security requirements, scalability needs, and existing infrastructure. For instance, if your primary concern is network isolation and security, OVN-Kubernetes might be the best fit. On the other hand, if you need a lightweight solution with minimal overhead, Flannel could be more suitable.

### Implementing and Managing Networking Plugins

Once you’ve chosen a networking plugin, the next step is implementation. This involves configuring the plugin within your OpenShift cluster and ensuring it aligns with your network policies. Regular monitoring and management are crucial to maintaining optimal performance and security. Utilizing OpenShift’s built-in tools and dashboards can greatly aid in this process, providing visibility into network traffic and potential bottlenecks.

Related: Before you proceed, you may find the following posts helpful for some pre-information

  1. Kubernetes Networking 101 
  2. Kubernetes Security Best Practice 
  3. Internet of Things Theory
  4. OpenStack Neutron
  5. Load Balancing
  6. ACI Cisco

OpenShift Networking

Networking Overview

1. Pod Networking: In OpenShift, containers are encapsulated within pods, the most minor deployable units. Each pod has its IP address and can communicate with other pods within the same project or across different projects. This enables seamless communication and collaboration between applications running on different pods.

2. Service Networking: OpenShift introduces the concept of services, which act as stable endpoints for accessing pods. Services provide a layer of abstraction, allowing applications to communicate with each other without worrying about the underlying infrastructure. With service networking, you can easily expose your applications to the outside world and manage traffic efficiently.

3. Ingress and Egress: OpenShift provides a robust routing infrastructure through its built-in Ingress Controller. It lets you define rules and policies for accessing your applications outside the cluster. To ensure seamless connectivity, you can easily configure routing paths, load balancing, SSL termination, and other advanced features.

4. Network Policies: OpenShift enables fine-grained control over network traffic through network policies. You can define rules to allow or deny communication between pods based on their labels and namespaces. This helps enforce security measures and isolate sensitive workloads from unauthorized access.

5. Multi-Cluster Networking: OpenShift allows you to connect multiple clusters, creating a unified networking fabric. This enables you to distribute your applications across different clusters, improving scalability and fault tolerance. OpenShift’s intuitive interface makes managing and monitoring your multi-cluster environment easy.

6. Network Policies and Security: One key aspect of Openshift Networking is its support for network policies. These policies allow administrators to define fine-grained rules and access controls, ensuring secure communication between different components within the cluster. With Openshift Networking, organizations can enforce policies restricting traffic flow, implementing encryption mechanisms, and safeguarding sensitive data from unauthorized access.

7. Service Discovery and Load Balancing: Service discovery and load balancing are crucial for maintaining high availability and optimal performance in a dynamic container environment. Openshift Networking offers robust mechanisms for service discovery, allowing containers to locate and communicate with one another seamlessly. Additionally, it provides load-balancing capabilities to distribute incoming traffic efficiently across multiple instances, ensuring optimal resource utilization and preventing bottlenecks.

8. Network Plugin Options: Openshift Networking offers a range of network plugin options, allowing organizations to select the most suitable solution for their specific requirements. Whether the native OpenShift SDN (Software-Defined Networking) plugin, the popular Flannel plugin, or the versatile Calico plugin, Openshift provides flexibility and compatibility with different network architectures and setups.

Example Network Policy Technology: GKE Kubernetes

Kubernetes network policy

POD Networking

Each pod in Kubernetes is assigned an IP address from an internal network that allows pods to communicate with each other. By doing this, all containers within the pod behave as if they were on the same host. The IP address of each pod enables pods to be treated like physical hosts or virtual machines.

It includes port allocation, networking, naming, service discovery, load balancing, application configuration, and migration. Linking pods together is unnecessary, and IP addresses shouldn’t be used to communicate directly between pods. Instead, create a service to interact with the pods.

OpenShift Container Platform DNS

To enable the frontend pods to communicate with the backend services when running multiple services, such as frontend and backend, environment variables are created for user names, service IPs, and more. To pick up the updated values for the service IP environment variable, the frontend pods must be recreated if the service is deleted and recreated.

To ensure that the IP address for the backend service is generated correctly and that it can be passed to the frontend pods as an environment variable, the backend service must be created before any frontend pods.

Due to this, the OpenShift Container Platform has a built-in DNS, enabling the service to be reached by both the service DNS and the service IP/port. Split DNS is supported by the OpenShift Container Platform by running SkyDNS on the master, which answers DNS queries for services. By default, the master listens on port 53.

POD Network & POD Communication

As a general rule, pod-to-pod communication holds for all Kubernetes clusters: An IP address is assigned to each Pod in Kubernetes. While pods can communicate directly with each other by addressing their IP addresses, it is recommended that they use Services instead. Services consist of Pods accessed through a single, fixed DNS name or IP address. The majority of Kubernetes applications use Services to communicate. Since Pods can be restarted frequently, addressing them directly by name or IP is highly brittle. Instead, use a Service to manage another pod.

Simple pod-to-pod communication

The first thing to understand is how Pods communicate within Kubernetes. Kubernetes provides IP addresses for each Pod. IP addresses are used to communicate between pods at a very primitive level. Therefore, you can directly address another Pod using its IP address whenever needed.

A Pod has the same characteristics as a virtual machine (VM), which has an IP address, exposes ports, and interacts with other VMs on the network via IP address and port.

What is the communication mechanism between the front-end pod and the back-end pod? In a web application architecture, a front-end application is expected to talk to a backend, an API, or a database. In Kubernetes, the front and back end would be separated into two Pods.

The front end could be configured to communicate directly with the back end via its IP address. However, a front end would still need to know the backend’s IP address, which can be tricky when the Pod is restarted or moved to another node. Using a Service can make our solution less brittle.

Because the app still communicates with the API pods via the Service, which has a stable IP address, if the Pods die or need to be restarted, this won’t affect the app.

pod networking
Diagram: Pod networking. Source is tutorialworks

How do containers in the same Pod communicate?

Sometimes, you may need to run multiple containers in the same Pod. The IP addresses of various containers in the same Pod are the same, so Localhost can be used to communicate between them. For example, a container in a pod can use the address localhost:8080 to communicate with another container in the Pod on port 8080.

Two containers cannot share the same port in the pod because the IP addresses are shared, and communication occurs on localhost. For instance, you wouldn’t be able to have two containers in the same Pod that expose port 8080. So, it would help if you ensured that the services use different ports.

In Kubernetes, pods can communicate with each other in a few different ways:

  1. Containers in the same Pod can connect using localhost; the other container exposes the port number.
  2. A container in a Pod can connect to another Pod using its IP address. To find the IP address of a pod, you can use oc get pods.
  3. A container can connect to another Pod through a Service. Like my service, a service has an IP address and usually a DNS name.

OpenShift and Pod Networking

When you initially deploy OpenShift, a private pod network is created. Each pod in your OpenShift cluster is assigned an IP address on the pod network, which is used to communicate with each pod across the cluster.

The pod network spanned all nodes in your cluster and was extended to your second application node when that was added to the cluster. Your pod network IP addresses can’t be used on your network by any network that OpenShift might need to communicate with. OpenShift’s internal network routing follows all the rules of any network and multiple destinations for the same IP address lead to confusion.

**Endpoint Reachability**

Also, Endpoint Reachability. Not only have endpoints changed, but have the ways we reach them. The application stack previously had very few components, maybe just a cache, web server, or database. Using a load balancing algorithm, the most common network service allows a source to reach an application endpoint or load balance to several endpoints.

A simple round-robin or a load balancer that measured load was standard. Essentially, the sole purpose of the network was to provide endpoint reachability. However, changes inside the data center are driving networks and network services toward becoming more integrated with the application.

Nowadays, the network function exists no longer solely to satisfy endpoint reachability; it is fully integrated. In the case of Red Hat’s OpenShift, the network is represented as a Software-Defined Networking (SDN) layer. SDN means different things to different vendors. So, let me clarify in terms of OpenShift.

Highlighting software-defined network (SDN)

When you examine traditional networking devices, you see the control and forwarding planes shared on a single device. The concept of SDN separates these two planes, i.e., the control and forwarding planes are decoupled. They can now reside on different devices, bringing many performance and management benefits.

The benefits of network integration and decoupling make it much easier for the applications to be divided into several microservice components driving the microservices culture of application architecture. You could say that SDN was a requirement for microservices.

software defined networking
Diagram: Software Defined Networking (SDN). Source is Opennetworking

Challenges to Docker Networking 

Port mapping and NAT

Docker containers have been around for a while, but networking had significant drawbacks when they first came out. If you examine container networking, for example, Docker containers have other challenges when they connect to a bridge on the node where the docker daemon is running.

To allow network connectivity between those containers and any endpoint external to the node, we need to do some port mapping and Network Address Translation (NAT). This adds complexity.

Port Mapping and NAT have been around for ages. Introducing these networking functions will complicate container networking when running at scale. It is perfectly fine for 3 or 4 containers, but the production network will have many more endpoints. The origins of container networking are based on a simple architecture and primarily a single-host solution.

Docker at scale: Orchestration layer

The core building blocks of containers, such as namespaces and control groups, are battle-tested. Although the docker engine manages containers by facilitating Linux Kernel resources, it’s limited to a single host operating system. Once you get past three hosts, networking is hard to manage. Everything needs to be spun up in a particular order, and consistent network connectivity and security, regardless of the mobility of the workloads, are also challenged.

Docker Default networking
Diagram: Docker Default networking

This led to an orchestration layer. Just as a container is an abstraction over the physical machine, the container orchestration framework is an abstraction over the network. This brings us to the Kubernetes networking model, which Openshift takes advantage of and enhances; for example, the OpenShift Route Construct exposes applications for external access.

The Kubernetes model: Pod networking

As we discussed, the Kubernetes networking model was developed to simplify Docker container networking, which had drawbacks. It introduced the concept of Pod and Pod networking, allowing multiple containers inside a Pod to share an IP namespace. They can communicate with each other on IPC or localhost.

Nowadays, we place a single container into a pod, which acts as a boundary layer for any cluster parameters directly affecting the container. So, we run deployment against pods rather than containers.

In OpenShift, we can assign networking and security parameters to Pods that will affect the container inside. When an app is deployed on the cluster, each Pod gets an IP assigned, and each Pod could have different applications.

For example, Pod 1 could have a web front end, and Pod could be a database, so the Pods need to communicate. For this, we need a network and IP address. By default, Kubernetes allocates an internal IP address for each Pod for applications running within the Pod. Pods and their containers can network, but clients outside the cluster cannot access internal cluster resources by default. With Pod networking, every Pod must be able to communicate with each other Pod in the cluster without Network Address Translation (NAT).

A typical service type: ClusterIP

The most common service IP address type is “ClusterIP .” ClusterIP is a persistent virtual IP address used for load-balancing traffic internal to the cluster. Services with these service types cannot be directly accessed outside the cluster; there are other service types for that requirement.

The service type of Cluster-IP is considered for East-West traffic since it originates from Pods running in the cluster to the service IP backed by Pods that also run in the cluster.

Then, to enable external access to the cluster, we need to expose the services that the Pod or Pods represent, and this is done with an Openshift Route that provides a URL. So, we have a service in front of the pod or groups of pods. The default is for internal access only. Then, we have a URL-based route that gives the internal service external access.

Openshift load balancer
Diagram: Openshift networking and clusterIP. Source is Redhat.

Using an OpenShift Load Balancer

Get Traffic into the Cluster

If you do not need a specific external IP address, OpenShift Container Platform clusters can be accessed externally through an OpenShift load balancer service. The OpenShift load balancer allocates unique IP addresses from configured pools. Load balancers have a single edge router IP (which can be a virtual IP (VIP), but it is still a single machine for initial load balancing). How many OpenShift load balancers are there in OpenShift?

Two load balancers

The solution supports some load balancer configuration options: Use the playbooks to configure two load balancers for highly available production deployments, or use the playbooks to configure a single load balancer, which is helpful for proof-of-concept deployments. Deploy the solution using your OpenShift load balancer.

This process involves the following:

  1. The administrator performs the prerequisites;
  2. The developer creates a project and service if the service to be exposed does not exist;
  3. The developer exposes the service to create a route.
  4. The developer creates the Load Balancer Service.
  5. The network administrator configures networking to the service.

OpenShift load balancer: Different Openshift SDN networking modes

OpenShift security best practices  

So, depending on your Openshift SDN configuration, you can tailor the network topology differently. You can have free-for-all Pod connectivity, similar to a flat network or something stricter, with different security boundaries and restrictions. A free-for-all Pod connectivity between all projects might be good for a lab environment.

Still, you may need to tailor the network with segmentation for production networks with multiple projects, which can be done with one of the OpenShift SDN plugins. We will get to this in a moment.

Openshift networking does this with an SDN layer and enhances Kubernetes networking to have a virtual network across all the nodes created with the Open switch standard. For the Openshift SDN, this Pod network is established and maintained by the OpenShift SDN, configuring an overlay network using Open vSwitch (OVS).

The OpenShift SDN plugin

We mentioned that you could tailor the virtual network topology to suit your networking requirements. The OpenShift SDN plugin and the SDN model you select can determine this. 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. How are all these plugins enabled? The Openshift Container Platform (OCP) networking relies on the Kubernetes CNI model while supporting several plugins by default and several commercial SDN implementations, including Cisco ACI.

The native plugins rely on the virtual switch Open vSwitch and offer alternatives to providing segmentation using VXLAN, specifically the VNID or the Kubernetes Network Policy objects:

We have, for example:

        • ovs-subnet  

        • ovs-multitenant  

        • ovs-network policy

Choosing the right plugin depends on your security and control goals. As SDNs take over networking, third-party vendors develop programmable network solutions. OpenShift is tightly integrated with products from such providers by Red Hat. According to Red Hat, the following solutions are production-ready:

  1. Nokia Nuage
  2. Cisco Contiv
  3. Juniper Contrail
  4. Tigera Calico
  5. VMWare NSX-T
  6. ovs-subnet plugin

After OpenShift is installed, this plugin is enabled by default. As a result, pods can be connected across the entire cluster without limitations so traffic can flow freely between them. This may be undesirable if security is a top priority in large multitenant environments. 

  • ovs-multitenant plugin

Security is usually unimportant in PoCs and sandboxes but becomes paramount when large enterprises have diverse teams and project portfolios, especially when third parties develop specific applications. A multitenant plugin like ovs-multitenant is an excellent choice if simply separating projects is all you need.

This plugin sets up flow rules on the br0 bridge to ensure that only traffic between pods with the same VNID is permitted, unlike the ovs-subnet plugin, which passes all traffic across all pods. It also assigns the same VNID to all pods for each project, keeping them unique across projects.

  • ovs-networkpolicy plugin

While the ovs-multitenant plugin provides a simple and largely adequate means for managing access between projects, it does not allow granular control over access. In this case, the ovs-networkpolicy plugin can be used to create custom NetworkPolicy objects that, for example, apply restrictions to traffic egressing or entering the network.

  • Egress routers

In OpenShift, routers direct ingress traffic from external clients to services, which then forward it to pods. As well as forwarding egress traffic from pods to external networks, OpenShift offers a reverse type of router. Egress routers, on the other hand, are implemented using Squid instead of HAProxy. Routers with egress capabilities can be helpful in the following situations:

They are masking different external resources used by several applications with a single global resource. For example, applications may be developed so that they are built, pulling dependencies from other mirrors, and collaboration between their development teams is rather loose. So, instead of getting them to use the same mirror, an operations team can set up an egress router to intercept all traffic directed to those mirrors and redirect it to the same site.

To redirect all suspicious requests for specific sites to the audit system for further analysis.

OpenShift supports the following types of egress routers:

  • redirect for redirecting traffic to a specific destination IP
  • http-proxy for proxying HTTP, HTTPS, and DNS traffic

Summary:OpenShift Networking

In the ever-evolving world of cloud computing, Openshift has emerged as a robust application development and deployment platform. One crucial aspect that makes it stand out is its networking capabilities. In this blog post, we delved into the intricacies of Openshift networking, exploring its key components, features, and benefits.

Understanding Openshift Networking Fundamentals

Openshift networking operates on a robust and flexible architecture that enables efficient communication between various components within a cluster. It utilizes a combination of software-defined networking (SDN) and network overlays to create a scalable and resilient network infrastructure.

Exploring Networking Models in Openshift

Openshift offers different networking models to suit various deployment scenarios. The most common models include Single-Stacked Networking, Dual-Stacked Networking, and Multus CNI. Each model has advantages and considerations, allowing administrators to choose the most suitable option for their specific requirements.

Deep Dive into Openshift SDN

At the core of Openshift networking lies the Software-Defined Networking (SDN) solution. It provides the necessary tools and mechanisms to manage network traffic, implement security policies, and enable efficient communication between Pods and Services. We will explore the inner workings of Openshift SDN, including its components like the SDN controller, virtual Ethernet bridges, and IP routing.

Network Policies in Openshift

To ensure secure and controlled communication between Pods, Openshift implements Network Policies. These policies define rules and regulations for network traffic, allowing administrators to enforce fine-grained access controls and segmentation. We will discuss the concept of Network Policies, their syntax, and practical examples to showcase their effectiveness.

Conclusion: Openshift’s networking capabilities play a crucial role in enabling seamless communication and connectivity within a cluster. By understanding the fundamentals, exploring different networking models, and harnessing the power of SDN and Network Policies, administrators can leverage Openshift’s networking features to build robust and scalable applications.

In conclusion, Openshift networking opens up a world of possibilities for developers and administrators, empowering them to create resilient and interconnected environments. By diving deep into its intricacies, one can unlock the full potential of Openshift networking and maximize the efficiency of their applications.

auto scaling observability

Auto Scaling Observability

Autoscaling Observability

In today's digital landscape, where applications and systems are becoming increasingly complex and dynamic, the need for efficient auto scaling observability has never been more critical. This blog post will delve into the fascinating world of auto scaling observability, exploring its importance, key components, and best practices. Let's embark on this journey together!

Auto scaling observability is the practice of monitoring and gathering data about the performance, health, and behavior of an application or system as it dynamically scales. It enables organizations to gain deep insights into their infrastructure, ensuring optimal performance, resource allocation, and cost-efficiency.

Data Collection and Monitoring: The foundation of auto scaling observability lies in effectively collecting and monitoring data from various sources, including metrics, logs, and traces. This allows for real-time visibility into the system's behavior and performance.

Metrics and Alerting: Metrics play a crucial role in understanding the health and performance of an application or system. By defining relevant metrics and setting up proactive alerts, organizations can quickly identify anomalies and take necessary actions.

Logs and Log Analysis: Logs provide a wealth of information about the internal workings of an application or system. Leveraging log analysis tools and techniques enables organizations to detect errors, troubleshoot issues, and gain valuable insights for optimization.

Distributed Tracing: In complex distributed systems, tracing requests across various services becomes essential. Distributed tracing enables end-to-end visibility, helping organizations identify bottlenecks, latency issues, and optimize system performance.

Define Clear Observability Goals: Before implementing auto scaling observability, it's crucial to define clear goals and objectives. This will ensure that the selected tools and strategies align with the organization's specific needs and requirements.

Choose the Right Monitoring Tools: There is a plethora of monitoring tools available in the market, each with its own strengths and features. Consider factors such as scalability, ease of integration, and customization options when selecting the right tool for your auto scaling observability needs.

Implement Robust Alerting Mechanisms: Setting up effective alerts is vital for timely responses to critical events. Define meaningful thresholds and ensure that alerts are routed to the appropriate teams or individuals for prompt action.

Embrace Automation: Auto scaling observability thrives on automation. Leverage automation tools and frameworks to streamline data collection, analysis, and alerting processes. This will save time, reduce human error, and enable faster decision-making.

Auto scaling observability has emerged as a crucial aspect of managing modern applications and systems. By embracing the art of auto scaling observability, organizations can unlock the power of data insights, optimize performance, and enhance overall system reliability. So, take the first step towards a more observant future, and witness the transformation it brings to your digital landscape.

Highlights: Auto-scaling Observability

Understanding Auto-Scaling Observability

1: ) Auto-scaling observability refers to a system’s ability to adjust its resources based on real-time monitoring and analysis automatically. It combines two essential components: auto-scaling, which dynamically allocates resources, and observability, which provides insights into the system’s behavior and performance.

2: ) By leveraging advanced monitoring tools and intelligent algorithms, auto-scaling observability enables organizations to optimize resource allocation and respond swiftly to changing demands.

3: ) Auto scaling observability involves the continuous monitoring and analysis of system metrics to automate the scaling of resources. This process allows organizations to automatically increase or decrease their computing power based on current demand, ensuring that applications run smoothly without over-provisioning or incurring unnecessary costs.

Auto-Scaling – Key Points:

– Enhanced Scalability and Performance: Auto-scaling observability allows systems to scale resources up or down based on actual usage patterns. This ensures that the system can handle peak loads efficiently without overprovisioning resources during periods of low demand. Organizations can avoid costly downtime by dynamically adjusting resources and ensuring optimal performance during sudden traffic spikes.

– Cost Optimization: With auto-scaling observability, businesses can significantly reduce infrastructure costs. Organizations can avoid unnecessary idle resource expenditures by accurately provisioning resources based on real-time data. This cost optimization approach ensures that companies only pay for the resources required, resulting in considerable savings.

– Improved Fault Tolerance: Auto-scaling observability is crucial in enhancing system resilience. Organizations can promptly identify and address potential issues by continuously monitoring the system’s health. In case of anomalies or failures, the system can automatically scale resources or trigger alerts for immediate remediation. This proactive approach minimizes the impact of failures and enhances the system’s overall fault tolerance.

Auto-scaling Example: Scaling with Docker Swarm

Auto Scaling – Key Components:

To fully harness the power of auto scaling observability, it’s important to understand its key components. These include metrics collection, alerting, and automated responses. Let’s delve deeper into each of these elements:

1. **Metrics Collection:** Gathering data from various sources is the foundation of observability. This involves collecting CPU usage, memory utilization, network traffic, and other vital metrics. With comprehensive data, organizations can better understand their infrastructure’s behavior and make informed scaling decisions.

2. **Alerting:** Once data is collected, it’s essential to set up alerts for any anomalies or thresholds that are breached. Alerting enables teams to respond swiftly to potential issues, minimizing downtime and maintaining application performance.

3. **Automated Responses:** The ultimate goal of observability is to automate responses to fluctuating demands. By employing pre-defined rules and machine learning algorithms, businesses can ensure that resources are scaled up or down automatically, optimizing both performance and cost.

**The Role of the Metric**

“What Is a Metric: Good for Known” Regarding auto-scaling observability and metrics, one must understand the metric’s downfall. A metric is a single number, with tags optionally appended for grouping and searching those numbers. They are disposable and cheap and have a predictable storage footprint.

A metric is a numerical representation of a system state over a recorded time interval. It can tell you if a particular resource is over or underutilized at a specific moment. For example, CPU utilization might be at 75% right now.

Implementing Auto-Scaling Observability

Choosing the Right Monitoring Tools: To effectively implement auto-scaling observability, organizations must select appropriate monitoring tools that provide real-time insights into system performance, resource utilization, and user behavior. These tools should offer robust analytics capabilities and seamless integration with auto-scaling platforms.

Defining Metrics and Thresholds: Accurate metrics and thresholds are critical for successful auto-scaling observability. Organizations must identify key performance indicators (KPIs) that align with their business objectives and set appropriate thresholds for scaling actions. For example, CPU utilization, response time, and error rates are standard metrics for auto-scaling decisions.

Automating Scaling Actions: Organizations should automate scaling actions based on predefined rules to fully leverage the benefits of auto-scaling observability. By integrating monitoring tools, auto-scaling platforms, and orchestration frameworks, businesses can ensure that resource allocation adjustments are performed seamlessly and without human intervention.

Service Mesh & Auto-Scaling

Service mesh acts as a dedicated infrastructure layer for managing service-to-service communications. It provides a suite of capabilities, including traffic management, security, and, most importantly, observability. By integrating a service mesh, such as Istio or Linkerd, into your auto scaling environment, you gain granular visibility into your microservices architecture. This includes detailed metrics, tracing, and logging, enabling you to monitor traffic patterns, latency, and error rates with precision.

### Implementing Service Mesh for Optimal Observability

Deploying a service mesh involves several considerations to maximize its observability benefits. Start by identifying the microservices that will benefit most from enhanced observability. Next, configure the service mesh to collect and process telemetry data effectively. Ensure your observability stack—comprising metrics, logs, and traces—is equipped to handle the data influx. Finally, leverage the insights gained to optimize your auto scaling strategy, ensuring minimal downtime and optimal performance.

### What is a Cloud Service Mesh?

A cloud service mesh is a dedicated infrastructure layer that manages service-to-service communication within a distributed application. It decouples the networking logic from the application code, enabling developers to focus on core functionality without worrying about the complexities of inter-service communication. Service meshes provide features like load balancing, service discovery, and security policies, making them indispensable for modern cloud-native applications.

### Key Benefits of Service Mesh

#### Simplified Networking

One of the primary benefits of a service mesh is the simplification of networking within a microservices architecture. By abstracting the communication logic, service meshes make it easier to manage and scale applications. Developers can implement features like retries, timeouts, and circuit breakers without modifying their application code.

#### Enhanced Security

Service meshes provide robust security features, including mutual TLS (mTLS) for service-to-service encryption and authentication. This ensures that communication between services is secure by default, reducing the risk of data breaches and unauthorized access.

#### Traffic Management

With a service mesh, you can intelligently route traffic between services based on various criteria such as load, service version, or geographic location. This level of control enables canary deployments, blue-green deployments, and A/B testing, making it easier to roll out new features with minimal risk.

### The Role of Observability

Observability is the ability to measure the internal states of a system based on the outputs it produces. In the context of a service mesh, observability involves collecting and analyzing metrics, logs, and traces to gain insight into the performance and behavior of the services.

### Why Observability Matters

Without proper observability, managing a service mesh can become a daunting task. Observability allows you to monitor the health of your services, detect anomalies, and troubleshoot issues in real-time. It provides the visibility needed to ensure that your service mesh is functioning as intended and that any problems are quickly identified and resolved.

### Tools and Techniques

Several tools can enhance observability in a service mesh, such as Prometheus for metrics, Jaeger for tracing, and Fluentd for logging. Combining these tools provides a comprehensive view of your service mesh’s performance and health, enabling proactive maintenance and quicker issue resolution.

Gaining Visibility with Google Ops Agent

**The Role of Google Ops Agent in Observability**

Google Ops Agent is a unified agent that simplifies the process of collecting telemetry data from your cloud environment. Its ability to seamlessly integrate with Google Cloud’s operations suite makes it an essential tool for businesses looking to enhance their auto-scaling observability. By providing detailed insights into CPU usage, memory utilization, and network traffic, Google Ops Agent ensures that your infrastructure is both monitored and optimized in real-time.

**Benefits of Enhanced Observability**

With Google Ops Agent in place, businesses gain a clearer picture of how their auto-scaling systems are functioning. This enhanced observability translates to several benefits: improved system reliability, faster troubleshooting, and better resource allocation. By actively monitoring metrics and logs, organizations can preemptively address issues before they escalate into significant problems. Furthermore, insights derived from this data can inform future scaling strategies and infrastructure investments.

**Example: Understanding Ops Agent**

Ops Agent is a lightweight and efficient monitoring agent developed by Google Cloud. It enables you to collect crucial metrics and logs from your Compute Engine instances, providing valuable insights into their performance and health. By leveraging Ops Agent, you can proactively detect issues, troubleshoot problems, and optimize the utilization of your instances.

To begin monitoring your Compute Engine instances with Ops Agent, install it on your virtual machines. The installation process is straightforward and can be done using package managers like apt or yum. Once installed, Ops Agent seamlessly integrates with Google Cloud Monitoring, allowing you to access and analyze the gathered data.

After installing Ops Agent, it is essential to configure the monitoring metrics that you want to collect. Ops Agent supports many metrics, including CPU usage, memory utilization, disk I/O, and network traffic. By tailoring the metrics collection to your specific needs, you can efficiently monitor the performance of your Compute Engine instances and identify any anomalies or bottlenecks.

What is GKE-Native Monitoring?

GKE-Native Monitoring is a powerful monitoring solution provided by Google Cloud Platform (GCP) designed explicitly for GKE clusters. It leverages the capabilities of Prometheus and Stackdriver, offering a unified monitoring experience within the GCP ecosystem. With GKE-Native Monitoring, users can effortlessly collect, visualize, and analyze metrics and logs related to their GKE clusters, enabling them to make data-driven decisions and proactively address any issues that may arise.

GKE-Native Monitoring offers a range of features that enhance observability and simplify monitoring workflows. Some notable features include:

1. Automatic Metric Collection: GKE-Native Monitoring automatically collects a rich set of metrics from every GKE cluster, including CPU and memory utilization, network traffic, and application-specific metrics. This eliminates the need for manual configuration and ensures comprehensive monitoring that is out of the box.

2. Custom Metrics and Alerts: Users can define custom metrics and alerts tailored to their applications and business requirements. This empowers them to monitor critical aspects of their clusters and receive notifications when predefined thresholds are crossed, enabling timely actions and proactive troubleshooting.

3. Integration with Stackdriver Logging: GKE-Native Monitoring integrates with Stackdriver Logging, allowing users to correlate log data with metrics. By combining logs and metrics, users can gain a holistic view of their application’s behavior and quickly identify the root causes of any issues.

Kubernetes Autoscaling

Kubernetes auto scaling involves dynamically adjusting the number of running pods in a cluster based on current demand. This ensures that applications remain responsive while optimizing resource utilization. With the right configuration, auto scaling can help maintain performance during traffic spikes and reduce costs during low-traffic periods.

**Benefits of Implementing Auto Scaling**

The implementation of Kubernetes auto scaling brings a multitude of benefits to organizations. Firstly, it enhances resource efficiency by ensuring that applications use only the necessary resources, reducing wastage and lowering operational costs. Secondly, it improves application performance and reliability by adapting to traffic fluctuations, ensuring a consistent user experience even during peak demand. Moreover, auto scaling supports rapid scaling for new deployments, enabling businesses to respond swiftly to market changes without manual intervention.

**Challenges and Best Practices**

While Kubernetes auto scaling offers significant advantages, it also presents challenges that organizations need to navigate. One common issue is configuring the right scaling metrics and thresholds to avoid over-provisioning or under-provisioning resources. It’s crucial to thoroughly test and monitor these settings in a staging environment before deploying them to production. Additionally, consider using custom metrics that align closely with your application’s performance indicators for more accurate scaling decisions. Regularly reviewing and updating your scaling policies ensures they remain effective as application workloads evolve.

### Types of Auto Scaling in Kubernetes

Kubernetes offers several types of auto scaling, each designed to address different aspects of resource management. The most commonly used are Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), and Cluster Autoscaler.

1. **Horizontal Pod Autoscaler (HPA):** HPA adjusts the number of pod replicas in a deployment or replication controller based on observed CPU utilization or other select metrics. It’s ideal for applications with varying workloads, ensuring that resources are available when needed and conserved when demand is low.

2. **Vertical Pod Autoscaler (VPA):** VPA automatically adjusts the CPU and memory requests and limits for containers within pods. This ensures that each pod has the right amount of resources, preventing over-provisioning and under-provisioning.

3. **Cluster Autoscaler:** This tool automatically adjusts the size of the Kubernetes cluster so that all pods have a place to run. It adds nodes when pods are unschedulable due to resource shortages and removes nodes when they’re underutilized.

### Configuring Auto Scaling in Kubernetes

To leverage Kubernetes auto scaling effectively, you’ll need to configure it to meet your application’s specific needs. The process typically involves setting up metrics and thresholds that trigger scaling actions.

For HPA, you’ll define the target CPU utilization or other custom metrics that the autoscaler should monitor. VPA requires setting up recommendations for resource requests and limits. Finally, the Cluster Autoscaler needs to be linked with your cloud provider to manage node scaling efficiently.

It’s crucial to regularly monitor and adjust these configurations to ensure optimal performance, as application demands can evolve over time.

### Best Practices for Kubernetes Auto Scaling

Implementing auto scaling in Kubernetes is not a set-it-and-forget-it task. Here are some best practices to consider:

– **Understand Your Workloads:** Analyze your application’s workload patterns to choose the right type of auto scaling and set appropriate thresholds.

– **Use Custom Metrics:** While CPU and memory are common metrics, consider using application-specific metrics to drive more accurate scaling decisions.

– **Test and Monitor:** Regularly test your auto scaling configurations in a non-production environment. Continuous monitoring and logging are essential to catch and resolve issues early.

You can dynamically scale up or down any architecture component through autoscaling.

An example of a good use of autoscaling is as follows:

You may need additional web servers to handle the surge in traffic at the end of the day when your website’s load increases. Where does the rest of the day fit in? Your servers can’t sit idle during most business hours. Especially if you’re using a cloud provider, you want to optimize your environment’s potential costs. An autoscale allows you to increase the number of components during a spike and scale down during a regular period.

Example: Prometheus Pull Approach

There can be many tools to gather metrics, such as Prometheus, along with several techniques used to collect these metrics, such as the PUSH and PULL approaches. There are pros and cons to each method. However, Prometheus metric types and its PULL approach are prevalent in the market. However, if you want full observability and controllability, remember it is solely in metrics-based monitoring solutions.  For additional information on Monitoring and Observability and their difference, visit this post on observability vs monitoring.

These autoscalers rely on Kubernetes’ metric server for scaling up or down k8s objects.

Adopting Auto-scaling

Autoscaling is a mechanism that automatically adjusts the number of computing resources allocated to an application based on its demand. By dynamically scaling resources up or down, autoscaling enables organizations to handle fluctuating workloads efficiently. However, robust observability is crucial to harness the power of autoscaling truly.

The Role of Observability in Autoscaling

Observability is the ability to gain insights into a system’s internal state based on its external outputs. It plays a pivotal role in understanding the system’s behavior, identifying bottlenecks, and making informed scaling decisions regarding autoscaling. It provides visibility into key metrics like CPU utilization, memory usage, and network traffic. With observability, you can make data-driven decisions and ensure optimal resource allocation.

 Monitoring and Metrics

To achieve effective autoscaling observability, comprehensive monitoring is essential. Monitoring tools collect various metrics, such as response times, error rates, and resource utilization, to provide a holistic view of your infrastructure. These metrics can be analyzed to identify patterns, detect anomalies, and trigger autoscaling actions when necessary. You can proactively address performance issues and optimize resource utilization by monitoring and analyzing metrics.

Logging and Tracing

In addition to monitoring, logging, and tracing are critical components of autoscaling observability. Logging captures detailed information about system events, errors, and activities, enabling you to troubleshoot issues and gain insights into system behavior. Tracing helps you understand the flow of requests across different services. Logging and tracing provide a granular view of your application’s performance, aiding in autoscaling decisions and ensuring smooth operation.

Automation and Alerting

Automation and alerting mechanisms are vital to mastering autoscaling observability. You can configure thresholds and triggers that initiate autoscaling actions based on predefined conditions by setting up automated processes. This allows for proactive scaling, ensuring your system is constantly optimized for performance. Additionally, timely alerts can notify you of critical events or anomalies, enabling you to take immediate action and maintain the desired scalability.

Autoscaling observability is the key to unlocking its true potential. By understanding your system’s behavior through comprehensive monitoring, logging, and tracing, you can make informed decisions and ensure optimal resource allocation. With automation and alerting mechanisms, you can proactively respond to changing demands and maintain high efficiency. Embrace autoscaling observability and take your infrastructure management to new heights.

Managed Instance Groups

### Auto Scaling: Adapting to Your Needs

One of the standout features of managed instance groups is auto scaling. With auto scaling, your infrastructure can dynamically adjust to the current demand. This ensures that your applications have the necessary resources without overspending. By setting up policies based on CPU usage, requests per second, or custom metrics, MIGs can efficiently allocate resources, keeping your applications responsive and your costs under control.

### Observability: Keeping a Close Watch

Observability is key in maintaining the health of your cloud infrastructure. Google Cloud’s managed instance groups provide comprehensive monitoring tools that give you insights into the performance and stability of your instances. By leveraging metrics, logs, and traces, you can detect anomalies, optimize performance, and ensure your applications run smoothly. This proactive approach to monitoring allows you to address potential issues before they impact your services.

### Integration with Google Cloud

Managed instance groups seamlessly integrate with various Google Cloud services, enhancing their utility and flexibility. From load balancing to deploying containerized applications with Google Kubernetes Engine, MIGs work in tandem with Google’s ecosystem to provide a cohesive and powerful cloud solution. This integration not only simplifies management but also boosts the scalability and reliability of your applications.

Managed Instance Group

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

  1. Load Balancing
  2. Microservices Observability
  3. Network Functions
  4. Distributed Systems Observability

Autoscaling Observability

Understanding Autoscaling

-Before we discuss observability, let’s briefly explore the concept of autoscaling. Autoscaling refers to the ability of an application or infrastructure to automatically adjust its resources based on demand. It enables organizations to handle fluctuating workloads and optimize resource allocation efficiently.

-Observability, in the context of autoscaling, refers to gaining insights into an autoscaling system’s performance, health, and efficiency. It involves collecting, analyzing, and visualizing relevant data to understand the application and infrastructure’s behavior and patterns.

-Through observability, organizations can make informed decisions to optimize autoscaling algorithms, resource allocation, and overall system performance. To achieve effective autoscaling observability, several critical components come into play. These include:

A. Metrics and Monitoring: Gathering and monitoring key metrics such as CPU utilization, response times, request rates, and error rates is fundamental for understanding the application and infrastructure’s performance.

B. Logging and Tracing: Logging captures detailed information about events and transactions within the system, while tracing provides insights into the flow of requests across various components. Both logging and tracing contribute to a comprehensive understanding of system behavior.

C. Alerting and Thresholds: Setting up appropriate alerts and thresholds based on predefined criteria ensures timely notifications when specific conditions are met.  

Tools and Technologies for Autoscaling Observability

A wide range of tools and technologies are available to facilitate autoscaling observability. Prominent examples include Prometheus, Grafana, Elasticsearch, Kibana, and CloudWatch. These tools provide robust monitoring, visualization, and analysis capabilities, enabling organizations to gain deep insights into their autoscaling systems.

The first component of observability is the channels that convey observations to the observer. There are three channels: logs, traces, and metrics. These channels are common to all areas of observability, including data observability.

1.Logs: Logs are the most typical channel and take several forms (e.g., a line of free text or JSON). They are intended to encapsulate information about an event.

2.Traces: Traces allow you to do what logs don’t—reconnect the dots of a process. Because traces represent the link between all events of the same process, they allow the whole context to be derived from logs efficiently. Each pair of events, an operation, is a span that can be distributed across multiple servers.

3.Metrics: Finally, we have metrics. Every system state has some component that can be represented with numbers, and these numbers change as the state changes.

Understanding VPC Flow Logs

VPC Flow Logs capture information about the IP traffic going in and out of Virtual Private Clouds (VPCs) within Google Cloud. Enabling VPC Flow Logs allows you to gain visibility into network traffic at the subnet level, thereby facilitating network troubleshooting, security analysis, and performance monitoring.

Once the VPC Flow Logs are enabled and data starts flowing in, it’s time to tap into the potential of Google Cloud Logging. Using the appropriate filters and queries, you can sift through the vast amount of log data and extract meaningful insights. Whether it’s identifying suspicious traffic patterns, monitoring network performance metrics, or investigating security incidents, Google Cloud Logging provides a robust set of tools to facilitate these analyses.

Auto Scaling Observability

**Metrics: Resource Utilization Only**

– Metrics help us understand resource utilization. In a Kubernetes environment, these metrics are used for auto-healing and auto-scheduling. Monitoring performs several functions when it comes to metrics. First, it can collect, aggregate, and analyze metrics to identify known patterns that indicate troubling trends.

– The critical point here is that it shifts through known patterns. Then, based on a known event, metrics trigger alerts that notify when further investigation is needed. Finally, we have dashboards that display the metrics data trends adapted for visual consumption.

– These monitoring systems work well for identifying previously encountered known failures but don’t help as much for the unknown. Unknown failures are the norm today with disgruntled systems and complex system interactions.

– Metrics are suitable for dashboards, but there won’t be a predefined dashboard for unknowns, as it can’t track something it does not know about. Using metrics and dashboards like this is a reactive approach, yet it’s widely accepted as the norm. Monitoring is a reactive approach best suited for detecting known problems and previously identified patterns. 

**Metrics and intermittent problems**

– The metrics can help you determine whether a microservice is healthy or unhealthy within a microservices environment. Still, a metric will have difficulty telling you if a microservices function takes a long time to complete or if there is an intermittent problem with an upstream or downstream dependency. So, we need different tools to gather this type of information.

– We have an issue with auto-scaling metrics because they only look at individual microservices with a given set of attributes. So, they don’t give you a holistic view of the problem. For example, the application stack now exists in numerous locations and location types; we need a holistic viewpoint.

– A metric does not give this. For example, metrics track simplistic system states that indicate a service is running poorly or may be a leading indicator or an early warning signal. However, while those measures are easy to collect, they don’t turn out to be proper measures for triggering alerts.

Latency & Cloud Trace

Latency, in the context of applications, refers to the time it takes for a request to travel from the user to the server and back. Various factors, such as network delays, server processing time, and database queries influence it. Understanding latency is essential for developers to identify bottlenecks and optimize their applications for better performance.

Google Cloud Trace is a powerful tool provided by Google Cloud Platform that allows developers to analyze and diagnose application latency issues. By integrating Cloud Trace into their applications, developers can gain valuable insights into their code’s performance and identify areas for improvement.

Developers need to capture traces to analyze application latency effectively. Traces provide a detailed record of a request’s execution path, allowing developers to pinpoint the exact areas where latency occurs. With Cloud Trace, developers can easily capture and visualize traces in a user-friendly interface.

Auto-scaling metrics: Issues with dashboards

Useful only for a few metrics

So, these metrics are gathered and stored in time-series databases, and we have several dashboards to display these metrics. These dashboards were first built, and there weren’t many system metrics to worry about. You could have gotten away with 20 or so dashboards. But that was about it.

As a result, it was easy to see the critical data anyone should know about for any given service. Moreover, those systems were simple and did not have many moving parts. This contrasts with modern services that typically collect so many metrics that fitting them into the same dashboard is impossible.

Issues with aggregate metrics

So, we must find ways to fit all the metrics into a few dashboards. Here, the metrics are often pre-aggregated and averaged. However, the issue is that the aggregate values no longer provide meaningful visibility, even when we have filters and drill-downs. Therefore, we need to predeclare conditions that describe conditions we expect in the future. 

This is where we use instinctual practices based on past experiences and rely on gut feeling. Remember the network and software hero? It would help to avoid aggregation and averaging within the metrics store. On the other hand, we have percentiles that offer a richer view. Keep in mind, however, that they require raw data.

**Auto Scaling Observability: Any Question**

A: ) For auto-scaling observability, we take on an entirely different approach. They strive for other exploratory methods to find problems. Essentially, those operating observability systems don’t sit back and wait for an alert or something to happen. Instead, they are always actively looking and asking random questions to the observability system.

B: ) Observability tools should gather rich telemetry for every possible event, having full content of every request and then being able to store it and query it. In addition, these new auto-scaling observability tools are specifically designed to query against high-cardinality data. High cardinality allows you to interrogate your event data in any arbitrary way that we see fit. Now, we ask any questions about your system and inspect its corresponding state. 

**No predictions in advance**

C: ) Due to the nature of modern software systems, you want to understand any inner state and services without anticipating or predicting them in advance. For this, we need to gain valuable telemetry and use some new tools and technological capabilities to gather and interrogate this data once it has been collected. Telemetry needs to be constantly gathered in flexible ways to debug issues without predicting how failures may occur. 

D: ) Conditions affecting infrastructure health change infrequently and are relatively more straightforward to monitor. In addition, we have several well-established practices to predict, such as capacity planning and the ability to remediate automatically, e.g., auto-scaling in a Kubernetes environment. All of these can be used to tackle these types of known issues.

E: ) Due to its relatively predictable and slowly changing nature, the aggregated metrics approach monitors and alerts perfectly for infrastructure problems. So here, a metric-based system works well. Metrics-based systems and their associated signals help you see when capacity limits or known error conditions of underlying systems are being reached.

F: ) So, metrics-based systems work well for infrastructure problems that don’t change much but fall dramatically short in complex distributed systems. For these systems, you should opt for an observability and controllability platform. 

Summary: Autoscaling Observability

Auto-scaling has revolutionized how we manage cloud resources, allowing us to adjust capacity dynamically based on demand. However, ensuring optimal performance and efficiency in auto-scaling environments requires proper observability. In this blog post, we explored the importance of auto-scaling observability and how it can enhance the overall effectiveness of your infrastructure.

Understanding Auto Scaling Observability

To truly grasp the significance of auto scaling observability, we must first understand what it entails. Auto-scaling observability refers to monitoring and gaining insights into the behavior and performance of auto-scaling groups and their associated resources. It involves collecting and analyzing various metrics, logs, and events to gain a comprehensive view of your infrastructure’s health and performance.

Key Metrics for Auto-Scaling Observability

When it comes to auto scaling observability, specific metrics play a crucial role in assessing the efficiency and performance of your infrastructure. Metrics like average CPU utilization, network throughput, and request latency can provide valuable insights into resource utilization, bottlenecks, and overall system health. Monitoring these metrics enables you to make informed decisions about scaling actions and resource allocation.

Implementing Effective Monitoring and Alerting

To achieve optimal auto-scaling observability, robust monitoring, and alerting mechanisms are essential. This involves setting up monitoring tools that can collect and analyze relevant metrics in real time. Configuring intelligent alerting systems can notify you of any anomalies or issues requiring immediate attention. By proactively monitoring and alerting, you can identify and address potential problems before they impact your system’s performance.

Leveraging Logging and Tracing for Deep Insights

Besides metrics, logging, and tracing provide deeper insights into the behavior and interactions within your auto-scaling environment. By capturing detailed logs and tracing requests across various services, you can gain visibility into the data flow and identify potential bottlenecks or errors. Proper logging and tracing practices can help troubleshoot issues, optimize performance, and enhance the overall reliability of your infrastructure.

Scaling Observability for Greater Efficiency

Auto-scaling observability is not a one-time setup; it requires continuous refinement and scaling alongside your infrastructure. As your system evolves, adapting your observability practices to match the changing demands is crucial. This may involve configuring additional monitoring tools, fine-tuning alert thresholds, or expanding log retention. By scaling observability in parallel with your infrastructure, you can ensure its efficiency and effectiveness in the long run.

Conclusion

In conclusion, auto scaling observability is critical to managing and optimizing cloud resources. Investing in proper monitoring, alerting, logging, and tracing practices can unlock the full potential of auto scaling. Improved observability leads to enhanced efficiency, performance, and reliability of your infrastructure, ultimately enabling you to provide better experiences to your users.

ACI networks

ACI Networks

ACI Networks

ACI networks, short for Application Centric Infrastructure networks, have emerged as a game-changer in the realm of connectivity. With their innovative approach to networking architecture, ACI networks have opened up new possibilities for businesses and organizations of all sizes. In this blog post, we will explore the key features and benefits of ACI networks, delve into the underlying technology, and highlight real-world use cases that showcase their transformative potential.

ACI networks are built on the principle of application-centricity, where the network infrastructure is designed to align with the needs of the applications running on it. By focusing on applications rather than traditional network components, ACI networks offer improved scalability, agility, and automation. They enable organizations to seamlessly manage and optimize their network resources, resulting in enhanced performance and efficiency.

1. Policy-Based Automation: ACI networks allow administrators to define policies that automatically govern network behavior, eliminating the need for manual configurations and reducing human errors. This policy-driven approach simplifies network management and accelerates application deployment.

2. Scalability and Flexibility: ACI networks are designed to scale and adapt to changing business requirements seamlessly. Whether it's expanding your network infrastructure or integrating with cloud environments, ACI networks offer the flexibility to accommodate growth and evolving needs.

3. Enhanced Security: ACI networks incorporate advanced security measures, such as microsegmentation and end-to-end encryption, to protect critical assets and sensitive data. These built-in security features provide organizations with peace of mind in an increasingly interconnected digital landscape.

ACI networks have found application across various industries and sectors. Let's explore a few real-world use cases that highlight their versatility:

1. Data Centers: ACI networks have revolutionized data center management by simplifying network operations, increasing agility, and streamlining service delivery. With ACI networks, data center administrators can provision resources rapidly, automate workflows, and ensure optimal performance for critical applications.

2. Multi-Cloud Environments: In today's multi-cloud era, ACI networks facilitate seamless connectivity and consistent policies across different cloud platforms. They enable organizations to build a unified network fabric, simplifying workload migration, enhancing visibility, and ensuring consistent security policies.

3. Financial Services: The financial services industry demands robust and secure networks. ACI networks provide the necessary foundation for high-frequency trading, real-time analytics, and secure transactions. They offer low-latency connectivity, improved compliance, and simplified network management for financial institutions.

ACI networks have emerged as a transformative force in the world of networking, offering a host of benefits such as policy-based automation, scalability, flexibility, and enhanced security. With their application-centric approach, ACI networks enable organizations to unlock new levels of efficiency, agility, and performance. Whether in data centers, multi-cloud environments, or the financial services industry, ACI networks are paving the way for a more connected and empowered future.

Highlights: ACI Networks

ACI Main Components

-APIC controllers and underlay network infrastructure are the main components of ACI. Due to specialized forwarding chips, hardware-based underlay switching in ACI has a significant advantage over software-only solutions.

-As a result of Cisco’s own ASIC development, ACI has many advanced features, including security policy enforcement, microsegmentation, dynamic policy-based redirection (allowing external L4-L7 service devices to be inserted into the data path), and detailed flow analytics—in addition to performance and flexibility.

-ACI underlays require Nexus 9000 switches exclusively. There are Nexus 9500 modular switches and Nexus 9300 fixed 1U to 2U models available. The spine function in ACI fabric is handled by certain models and line cards, while leaves can be handled by others, and some can even be used for both functions at the same time.The combination of different leaf switches within the same fabric is not limited.

Cisco Data Center Design

**The rise of virtualization**