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 pods 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 service discovery, routing, 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.

However, a replicated architecture also has some considerations. First, data synchronization can become a challenge. If the application relies on shared data across clusters, data synchronization and consistency mechanisms need to be implemented to ensure data integrity. Second, running a full copy of the application in each cluster requires more resources than a split-by-service approach, as each cluster needs sufficient capacity to handle the entire application workload.

Split-by-Service Architecture

In a split-by-service architecture, the services or components of an application are divided and deployed across multiple clusters. Each cluster runs a subset of the application's services, and the clusters work together to form the complete application. The benefits of this approach include:

• Strong Isolation: Splitting services across clusters provides more robust isolation between different application parts. This is particularly useful when dealing with regulatory compliance requirements. For example, services handling sensitive data (e.g., PCI DSS-compliant services) can be isolated in a dedicated cluster. In contrast, the remaining services can be operated in separate clusters with less stringent compliance requirements.
• Independent Scalability: Each service can be scaled independently based on its specific resource requirements and usage patterns, allowing for more granular resource allocation and optimization.
• Faster Development Cycles: With a split-by-service architecture, individual development teams can work on and deploy their specific services into their own clusters without impacting other teams. This enables faster development cycles and reduces the risk of conflicts or dependencies between teams.

However, a split-by-service architecture also introduces some challenges:

• Increased Complexity: Managing and orchestrating services across multiple clusters can be more complex than a replicated architecture. Inter-service communication, data consistency, and distributed transactions must be carefully designed and implemented.
• Network Latency: As services are distributed across clusters, network latency between services can increase. This must be considered when designing the application architecture and choosing the appropriate communication protocols and patterns.
• Operational Overhead: Managing and monitoring multiple clusters running different services requires more operational effort and tooling than a replicated architecture.

Choosing between a replicated or split-by-service architecture depends on the application's specific needs, such as scalability requirements, compliance obligations, development team structure, and operational constraints. In some cases, a hybrid approach combining elements of both architectures can strike a balance between simplicity and isolation.

Configuring Multi-Cluster Kubernetes

When configuring and managing multi-cluster Kubernetes deployments, various challenges and approaches must be considered. These approaches can be broadly categorized into two main categories: Kubernetes-Centric and Network-Centric. Each category focuses on different aspects of multi-cluster configuration and offers distinct solutions.

Kubernetes-centric approaches aim to extend and enhance the core Kubernetes primitives to support multi-cluster use cases. The goal is to provide a centralized management plane that allows administrators to manage and control multiple Kubernetes clusters from a single point of control. Kubernetes-centric approaches focus on extending the Kubernetes API and control plane to enable centralized management and control of multiple clusters. They provide a higher level of abstraction and automation, simplifying the configuration and management of multi-cluster environments.

The Kubernetes Cluster Federation project, managed by the Kubernetes Multicluster Special Interest Group, takes this approach, as does Google’s Anthos project (via environs).

Network-centric approaches prioritize creating network connectivity between clusters to enable communication and collaboration between applications running in different clusters. These approaches leverage various networking technologies and service mesh solutions to establish inter-cluster connectivity. Some notable examples of network-centric approaches include:

Istio

Istio is a popular service mesh platform that provides advanced networking capabilities for multi-cluster architectures.

Istio has two different strategies for multi-cluster support: a replicated control plane and a shared control plane. A replicated control plane generally results in greater system availability and resilience. Istio provides powerful primitives for multi-cluster communication at the expense of complexity. In practice, application and deployment workflow changes are needed to fully take advantage of Istio multi-cluster.

Linkerd

Linkerd service mirroring is a simple but powerful approach requiring no application modification. Moreover, Linkerd supports using Edge Stack to connect traffic between clusters, enabling resilient application-level connectivity over the Internet.

With service mirroring, traffic can be automatically routed to a mirrored service in another cluster if the primary service becomes unavailable, ensuring continuous service availability.

Consul

Consul is a distributed service mesh and service discovery platform. Consul's mesh gateway feature enables secure communication between services across different clusters.

Consul Connect uses a VPN-like approach built around Consul Mesh Gateways to connect disparate clusters. This approach requires configuring Consul for data center federation so that different Consul instances can achieve strong consistency over a WAN.

Network-centric approaches focus on establishing network connectivity and enabling seamless communication between clusters. They leverage service mesh technologies and networking solutions to create a unified application network across multiple clusters, allowing services to collaborate and interact transparently.

Most organizations adopting multi-cluster are evaluating network-centric approaches. The primary reasons for this trend are the Federation project's lack of maturity and the fact that a GitOps approach to configuration management has become de rigueur for Kubernetes users.

A GitOps approach and some basic automation lend themselves to managing multiple clusters, as each cluster can be created from a standardized configuration. Thus, a centralized management plane does not reduce management overhead in a way proportional to the complexity it introduces.

Embrace Multi-Cluster Kubernetes for Your Unique Needs

As Kubernetes continues to be the de facto standard for container orchestration, organizations are increasingly exploring multi-cluster deployments to enhance availability, isolation, and scalability. While this article provides a comprehensive overview of the benefits, architectures, and configuration approaches for multi-cluster Kubernetes, it is just the beginning of your journey.

Every organization has unique requirements, constraints, and goals that shape its multi-cluster strategy. It is crucial to carefully evaluate your needs, such as geographic distribution, compliance obligations, team structures, and performance requirements, to determine the most suitable architecture and approach for your multi-cluster deployment.

Mastering multi-cluster Kubernetes is an ongoing process that requires continuous learning, experimentation, and adaptation. Use this article as a foundation to understand the key concepts, architectures, and approaches, but don't stop there. Dive deeper into the tools, techniques, and best practices that align with your organization's unique needs and goals. By embracing multi-cluster Kubernetes strategically and thoughtfully, you can unlock new availability, isolation, and scalability levels for your applications.