When starting out with Kubernetes, you've probably used a single cluster architecture. This architecture is easy to set up and allows you to enjoy the benefits of scaling and deploying your applications. If you've used this setup for a long time and on much larger projects, then you must have started to notice the cracks in this architecture.

As your project grows, you'll start to notice specific bottlenecks of a single Kubernetes cluster architecture. These bottlenecks include resource constraints, fault isolation, geographic distribution, and others. However, there's a solution—multi-cluster architecture.

In this article, you'll understand how multi-cluster architecture in Kubernetes solves these bottlenecks and why it's important. In the end, you'll have the right amount of information to help you decide whether to use a single or multi-cluster architecture in your project.

What are Kubernetes multi-clusters?

Multi-clusters in Kubernetes are the deployment of several clusters across different data centres or cloud regions. In terms of your application, it means using more than one Kubernetes cluster to deploy and manage your product.

A multi-cluster setup can really come in handy when looking to offer optimum uptime. By distributing your application across multiple clusters, you can ensure that your services remain available even if one cluster experiences a failure.

Why do you need multiple clusters?

There are some reasons why most engineers consider using a multi-cluster architecture. Below are some of these reasons:

Scalability

With multi-cluster setups, you can better distribute your resources across several clusters. This makes it possible to scale your application more effectively to take on higher traffic and prevent any cluster from becoming a bottleneck.

Reduced blast radius

Blast radius refers to the extent of impact that a failure in one part of a system can have on the rest of the system. It measures how far-reaching the consequences of an incident can be. By relying on a multi-cluster architecture, you ensure that only a limited part of your system gets affected in the event of an outage or security breach.

Geographical redundancy and disaster recovery

Deploying your clusters in different geographical regions ensures that your application can withstand regional failures, such as natural disasters or network outages. This geographical spread also ensures quick failover and data recovery from unaffected clusters.

Reduced latency

When you place several clusters close to end-users, you greatly reduce the time it takes to process requests on your applications. This is particularly beneficial for global applications that serve users from various locations around the world.

Isolation

Rather than using namespaces in a single Kubernetes cluster, multi-clusters can provide better isolation for various development stages (development, testing, production). This isolation method enhances security by reducing the risk of cross-environment contamination and unauthorized access.

Operational flexibility

When performing maintenance, updates, or any scaling operation, you can decide to carry it out on any one of your clusters. This ensures that you're not affecting the entire system. This flexibility offers smoother operations and less disruption to services.

What does a multi-cluster architecture look like?

In practice, let's say you're working on a project, and you want to use a multi-cluster architecture for it. You can use any method of provisioning your Kubernetes clusters, but in this case, you're relying on Amazon Elastic Kubernetes Service (EKS) and Google Kubernetes Engine (GKE).

Figure 1: Multi-cluster architecture between AWS and GCP

First, you start by provisioning the EKS cluster in AWS and the GKE cluster in Google Cloud. Both clusters are fully functional Kubernetes environments set up in their respective cloud providers.

Next, to enable communication between the clusters, you can set up VPN connections between the AWS VPC and the Google Cloud VPC. This establishes a secure link that allows the clusters to communicate with each other.

After this, you'll install Cilium as the networking plugin (CNI) on both the EKS and GKE clusters. This involves installing Cilium's agents and configuring the clusters to use Cilium for networking. At this point, Cilium doesn't bother about how both clusters are connected but rather if their endpoints are reachable.

You can then configure Cilium's cluster mesh feature, which will integrate the clusters into a single, unified network. This involves configuring Cilium to recognize the other cluster and allowing services and pods in the EKS cluster to communicate seamlessly with those in the GKE cluster.

Finally, you'll deploy applications or services across the clusters and verify that they can interact as expected. With Cilium's cluster mesh, your EKS and GKE clusters are now interconnected, and your multi-cluster setup should work perfectly.

When to use a multi-cluster architecture

Depending on your organization's goals or your application's requirements, you must consider certain measures when choosing your product's architecture.

High availability

Let's say the primary goal of your application - as it is with 100% of other products - is to offer maximum uptime and remain operational even during regional outages or disasters.

In this case, you can consider deploying clusters in multiple geographical regions to ensure that if one cluster goes down, others can take over, providing uninterrupted service.

Geographical distribution

If you already have a large organization with a global user base or you're growing quickly, you can minimize latency by serving users based on location.

By setting up clusters in various regions much closer to your users, you reduce latency and improve the user experience by routing traffic to the cluster closest to them.

Workload segmentation

If your application has unique requirements for its resources, security, or infrastructure, you can use distinct clusters to cater to the specific needs of different workloads. For instance, you can separate machine learning workloads from standard web applications.

Cross-cloud strategy

Let's say your organization currently relies on multiple cloud providers for specific reasons, such as preventing vendor lock-in, utilizing specific resources, or ensuring redundancy. You can consider deploying your clusters across the different cloud providers to leverage each provider's best features.

Conclusion

In this article, we defined a multi-cluster and showed how its architecture works in practice. We also discussed some reasons why you might consider using it and what to look out for when making your choice.

Always remember that a multi-cluster architecture is handy when you need to ensure high availability, minimize latency, and perform maintenance without downtime.