Mastering Kubernetes Multi-Cluster: Strategies for Improved Availability, Isolation, and Scalability.

If you deploy on Kubernetes, you are scaling your application. This usually means scaling pods and nodes within a cluster. This type of scaling allows you to handle increased workloads and provides a level of fault tolerance.

However, there are scenarios where scaling within a single cluster won’t be enough. This is where Kubernetes multi-cluster deployments come into play. Multi-cluster implementations allow you to improve availability, isolation, and scalability across your application.

Here, we want to examine the benefits of this approach for organizations, how to architect Kubernetes multi-cluster deployments, and the top deployment strategies.

What is Multi-Cluster?

Here’s how a single cluster deployment looks:

This is a straightforward deployment. In a single cluster deployment, you can scale your application by:

1. Horizontal Pod Autoscaling (HPA): Automatically adjusting the number of based on observed CPU utilization or custom metrics.
2. Vertical Pod Autoscaling (VPA): Automatically adjusting the CPU and memory resources requested by pods based on historical usage.
3. Cluster Autoscaling: Automatically adjust the number of nodes in the cluster based on the pods' resource demands.

A multi-cluster deployment uses two or more clusters:

In a multi-cluster deployment, traffic routing to different clusters can be achieved through a global load balancer or API gateway. These sit in front of the clusters and distribute incoming traffic based on predefined rules or policies. These rules can consider factors such as the geographic location of the user, the workload's requirements, or the current state of the clusters. An API gateway can be used as a central entry point for external traffic and can route requests to the appropriate cluster based on predefined rules or policies. The API gateway bridges clusters and provides a unified interface for accessing services across clusters.

Clusters in a multi-cluster deployment can live in different locations, depending on the organization's requirements and infrastructure setup. Some common placement strategies include:

• Regional Clusters: Clusters are deployed in different geographic regions to provide better performance and availability to users in those regions. This helps reduce latency and improves the user experience.
• Cloud Provider Clusters: Clusters are deployed across multiple cloud providers or a combination of on-premises and cloud environments. This allows organizations to leverage the benefits of different cloud platforms and avoid vendor lock-in.
• Edge Clusters: Clusters are deployed at the edge, closer to the users or data sources. This is particularly useful for scenarios that require low-latency processing or have data locality constraints.

Clusters in a multi-cluster deployment also need to communicate with each other to enable seamless operation and data exchange. This is usually implemented in the same way services within a cluster communicate–through a service mesh providing a unified communication layer. The service mesh handles , , and secure cluster communication.

Why Multi-Cluster?

While single-cluster scaling methods are effective, they have limitations.

The first is fault tolerance. A single cluster is a single point of failure. The entire application becomes unavailable if the cluster experiences an outage or catastrophic failure. There are often limited disaster recovery options, and recovering from a complete cluster failure is challenging and time-consuming.

The second is the scalability limits. Scaling nodes vertically is limited by the maximum capacity of the underlying infrastructure, while scaling nodes horizontally may be constrained by the data center's or cloud provider's capacity.

Finally, you can have availability and isolation issues. If your single cluster is in a data center in us-west-1, European users may experience higher latency and reduced performance. Compliance and data sovereignty problems can also exist when storing and processing data within specific geographic boundaries.

Additionally, all applications and environments within the cluster compete for the same set of resources, and resource-intensive applications can impact the performance of other applications running in the same cluster.

Organizations can deploy more Kubernetes clusters and treat them as disposable by adopting a multi-cluster approach. Organizations now talk of “treating clusters as cattle, not pets.” This approach results in several benefits.

1. Improved Operational Readiness: By standardizing cluster creation, the associated operational runbooks, troubleshooting, and tools are simplified. This eliminates common sources of operational error while reducing the cognitive load for support engineers and SREs, ultimately leading to improved overall response time to issues.
2. Increased Availability and Performance: Multi-cluster enables applications to be deployed in or across multiple availability zones and regions, improving application availability and regional performance for global applications.
3. Eliminate Vendor Lock-In: A multi-cluster strategy enables your organization to shift workloads between different Kubernetes vendors to take advantage of new capabilities and pricing offered by different vendors.
4. Isolation and Multi-Tenancy: Strong isolation guarantees simplify key operational processes like cluster and application upgrades. Moreover, isolation can reduce the blast radius of a cluster outage. Organizations with strong tenancy isolation requirements can route each tenant to their individual cluster.
5. Compliance: Cloud applications today must comply with many regulations and policies. A single cluster is unlikely to be able to comply with every regulation. A multi-cluster strategy reduces the scope of compliance for each cluster.

Multi-Cluster Application Architecture

When designing a multi-cluster application, there are two fundamental architectural approaches:

Replicated Architecture

In a replicated architecture, each cluster runs a complete and identical copy of the entire application in a replicated architecture. This means that the application's services, components, and dependencies are deployed and running independently in each cluster. The key advantages of this approach are:

• Scalability: The application can be easily scaled globally by replicating it into multiple availability zones or data centers. This allows the application to handle increased traffic and efficiently serve users from different geographic locations.
• High Availability: A replicated architecture enables failover and high availability when coupled with a health-aware global load balancer. If one cluster experiences an outage or becomes unresponsive, user traffic can be seamlessly routed to another healthy cluster, ensuring continuous service.

Simplified Deployment: Since each cluster runs an identical copy of the application, deployment, and management processes are simplified. Updates and changes can be rolled out consistently across all clusters.