Comprehensive Guide to Multicluster Management in Kubernetes

Kubernetes has revolutionized the way applications are deployed, scaled, and managed in cloud environments. It was initially designed by Google, and its primary function was to manage containerized applications across a single cluster. However, as businesses have grown and technology has advanced, it’s become necessary to manage applications across multiple clusters. Using multiple clusters—even in similar locations—provides better scalability, reliability, and workload isolation. This multicluster approach enables precise resource allocation, improved fault tolerance, and stricter compliance adherence, allowing businesses to meet the evolving needs of modern operations.

This article explores the intricacies of multicluster management in Kubernetes, highlighting its importance, challenges, and best practices.

#Why Have Multiple Kubernetes Clusters?

In a Kubernetes setup, a cluster is a set of node machines for running containerized applications. If you’re familiar with the concept of server farms, think of a Kubernetes cluster as a similar type of setup but for containerized applications. A node in a Kubernetes cluster can either be a virtual machine or a physical computer that acts as a worker machine. Each of these nodes has a kubelet, which is an agent that is responsible for managing the node and interacting with the Kubernetes control plane. The following diagram illustrates a typical Kubernetes cluster setup:

In Kubernetes, the control plane manages the records and status of all objects, ensuring they match their intended state. It has three main components:

• The API server (kube-apiserver)
• The controller manager (kube-controller-manager)
• The scheduler (kube-scheduler)

These components can operate on a single node or be distributed across multiple nodes for better reliability.

The API server provides APIs for managing the application lifecycle. It serves as the access point for the cluster, handling external client connections, authentication, and proxying to nodes, pods, and services.

Most system resources in Kubernetes come with metadata, a desired state, and a current state. Controllers primarily work to reconcile the current state with the desired state. The reconciliation process is initiated whenever discrepancies between these states are identified. While metadata provides ancillary information about the resources, the core function of controllers is to continuously and automatically adjust the current state to align with the specified desired state, ensuring the system self-heals and adheres to the user-defined configurations. There are different controllers that handle various aspects of managing a Kubernetes cluster, including nodes, autoscaling, and services. The controller manager monitors the cluster’s state and introduces necessary changes, while the cloud controller manager ensures smooth integration with public cloud platforms.

The scheduler distributes containers across the cluster’s nodes, considering resource availability and affinity specifications.

Kubernetes includes machines called cluster nodes that run application containers and are managed by the control plane. The kubelet controller running in every node is responsible for the container runtime, referred to as containerd, while pods are logical constructs that package up a single application and represent a running process on a cluster. Pods are ephemeral and can perform autoscaling, upgrades, and deployments. They can contain multiple containers and storage volumes and are the primary Kubernetes construct that developers interact with.

Managing multiple Kubernetes clusters can be a complex task, particularly when dealing with clusters located in different geographical regions or hosted by various cloud providers. While this arrangement provides flexibility and improved service availability, it also significantly amplifies the complexities involved in Kubernetes administration.

#The Problem with a Single Cluster Kubernetes Setup

A single cluster might seem sufficient, especially for small to medium-sized applications. However, as an application grows, several challenges arise with a single cluster setup. Here are some of them:

• Resource limitations: A single cluster has finite resources. As workloads increase, the cluster might run out of resources, affecting performance and availability.
• Blast radius: If something goes wrong in a single cluster setup, the entire application can be affected. For instance, a misconfiguration or critical component failure could lead to a complete application outage.
• Regulatory and data residency requirements: Certain applications need to store data in specific geographical locations due to regulatory requirements. A single cluster, located in one region, can’t meet these requirements.

#Advantages of Multiple Clusters

A multicluster setup can overcome these challenges and offers several advantages compared to a single cluster setup:

• High availability and disaster recovery: With multiple clusters, if one fails, another can take over. For example, if a cluster in the United States experiences an outage, a backup cluster in Europe can handle the traffic, ensuring users experience no downtime.
• Fault isolation: Issues in one cluster won’t impact others. If a bug is introduced in the development cluster, it won’t affect the production cluster.
• Scalability: As demand grows, more clusters can be added. During high-traffic events, like Black Friday sales, additional clusters can manage the surge in users in a particular region.
• Geolocation and data sovereignty: Multiple clusters ensure compliance with regional data regulations. European user data can be stored in European clusters, while Asian user data remains in Asia, ensuring compliance with local laws.
• Environment isolation: Dedicated clusters for development, testing, and production ensure no overlap, maintaining the integrity of each environment.

#Challenges of Multicluster Management

While having multiple clusters offers numerous advantages, managing these clusters can be challenging. It requires a thorough understanding of Kubernetes architecture and networking, as well as the ability to troubleshoot issues. The following are some of the key challenges of managing multiple clusters:

• Configuration complexity: Each cluster has its own set of configurations. Ensuring consistency across all of your clusters requires meticulous attention. For instance, if a network policy is updated, it must be uniformly applied across all clusters. Likewise, if a security patch is applied to one cluster, it must be applied to all others to prevent vulnerabilities.
• Resource optimization: Overprovisioning in one cluster while another is resource-starved can lead to performance issues. Additionally, an underutilized cluster can lead to unnecessary costs. So, you must distribute resources efficiently across all clusters.
• Consistent configuration: Differences in configurations between clusters can lead to unexpected behaviors. For instance, network issues can occur if a cluster is configured to use a different network plugin than others. Similarly, a cluster configured to use a different version of Kubernetes can lead to compatibility issues.
• Control plane availability: The Kubernetes control plane is responsible for managing the lifecycle of pods and nodes as well as scaling applications in the cluster. The control plane going down will cause the entire cluster to stop. You need to ensure high availability of the control plane for multicluster setups.
• Compliance: With clusters spread across regions, ensuring each cluster complies with local data laws is challenging. For instance, clusters located in Europe must comply with GDPR regulations. Similarly, clusters located in India must comply with India’s Digital Personal Data Protection Act.
• Isolation and fault tolerance: Although effective fault tolerance and isolation are significant benefits of multiple clusters, they can be challenging to achieve. Each cluster must remain isolated to prevent a fault in one from disrupting others. For example, a bug in the development cluster must not impact the production cluster, and if one cluster experiences downtime, it shouldn’t affect others. This requires careful design and robust safeguards to prevent cross-cluster interference and maintain each cluster’s independent operation.
• Access management: Implementing role-based access control (RBAC) is essential to limit access to cluster resources and ensure only authorized personnel can perform specific operations. However, managing RBAC across multiple clusters can be challenging. For instance, if a new user is added to one cluster, they must be added to all clusters to ensure they have the same access to all clusters.
• Image management: Ensuring the security of container images is vital. Leveraging public Docker images can be risky due to vulnerabilities. It’s essential to audit and verify images before deploying them in production clusters. Also, all clusters must use the same set of images to avoid compatibility issues.

#Best Practices for Managing a Multicluster Setup

Ensuring consistency, having effective resource management, and maintaining security and compliance can be complex. There are tools that can simplify some of the operational complexities of the process, such as Karmada and Cluster API, but careful planning and configuration are still necessary to ensure an effective multicluster setup. The following are some best practices that can help simplify the process.

#Unified Configuration Management

Using tools like Helm can help ensure consistent configurations across all clusters. Helm is a package manager for Kubernetes that allows you to define, install, and upgrade applications in a cluster. It can also manage configurations across multiple clusters, ensuring uniform operations.

Apart from Helm, there are other tools like Kustomize and KubeVela that can help manage configurations across clusters. Kustomize is a native Kubernetes configuration management tool that allows you to customize configurations for different environments. KubeVela is a cloud-native application deployment tool that allows you to define, deploy, and manage applications across multiple clusters.

Kubernetes operators are another popular option used for managing configurations. Operators are software extensions to Kubernetes that make use of custom resources to manage applications and their components. For instance, the Prometheus operator can be used to manage Prometheus and its components.

#Control Plane High Availability

The Kubernetes control plane consists of the API server, scheduler, and controller manager. It also includes a key-value data store, which is typically etcd. To ensure high availability of control plane services, you need to run multiple replicas of the services across availability zones. You should also use highly available etcd clusters for data storage redundancy and load balancers for load balancing. You can use tools like kubeadm to bootstrap clusters, ensuring the control plane remains available.

#Governance and Compliance Tools

Managing compliance across multiple clusters can be challenging. Tools like Open Policy Agent (OPA) can help ensure compliance. OPA is an open source policy engine that allows you to define, manage, and enforce policies across clusters. For instance, you can use OPA to ensure all clusters are configured with the same network policies. You can also use it to ensure all clusters are compliant with local data laws. Other alternatives to OPA include Kyverno and jsPolicy.

#Centralized Management

As your clusters grow, complexity can increase. Implement a centralized management system to keep track of all clusters, optimize observability, and ensure consistent governance. Tools like Rancher can help manage multiple clusters from a single dashboard. It allows you to manage clusters across different cloud providers, including AWS, Azure, and Google Cloud. It also allows you to manage clusters across different regions from a single dashboard and configure access control and resource quotas across clusters.

#Virtual Clusters

A virtual cluster is a self-contained Kubernetes cluster that operates within a specific namespace of another Kubernetes cluster. This relatively new approach enables the creation of multiple virtual clusters within a single Kubernetes cluster. Each virtual cluster is isolated from others, ensuring a fault in one doesn’t affect others. Each virtual cluster can also have its own set of configurations, allowing you to test different configurations without affecting others.

#Using vCluster to Create Virtual Clusters

Tools like Loft Lab’s vCluster can help create virtual clusters within a single Kubernetes cluster. vCluster also allows you to manage access control and resource quotas for each virtual cluster, ensuring effective resource management.

vCluster operates within a namespace on a host, running a StatefulSet that contains a pod with two main containers: the control plane and the syncer. The control plane, by default, utilizes the API server and controller manager from K3s, with SQLite as its data store, though other storage backends like etcd, MySQL, and PostgreSQL can be used. Instead of a traditional scheduler, vCluster employs the syncer to manage pod scheduling. This syncer replicates pods created in the virtual cluster to the host, with the host’s scheduler handling the actual pod scheduling. The syncer ensures synchronization between the vCluster pod and the host pod. Additionally, each virtual cluster has its own CoreDNS pod that is used to resolve DNS requests within the virtual cluster.

The host manages several aspects of the clusters:

• Storage class: vCluster users can utilize the host’s storage class by default, which can also be modified with specific sync settings.
• Communication: Pod-to-pod or pod-to-service communication is managed by the host.
• Container runtime and networking: vCluster leverages the host’s container runtime and networking.
• Network isolation: To prevent communication between virtual clusters or communication with the host’s pods, a network policy should be applied to the host’s namespace.
• Resource management: To prevent virtual clusters from using all of the host’s resources, resource quotas and limit ranges can be set on the host’s namespace where vCluster operates.

The following diagram illustrates the internal workings of vCluster, showcasing components like the API server, data store, controller manager, and syncer. It also depicts how the syncer, with the host cluster’s scheduler, manages pod scheduling on the host:

Image courtesy of Loft

#Conclusion

While multicluster Kubernetes offers numerous benefits, such as improved reliability and better resource utilization, it also comes with challenges like applying consistent configurations, managing compliance, and enforcing consistent access control. You should carefully evaluate your needs and the available solutions to implement a successful multicluster strategy.

Tools like vCluster and OPA can simplify this process, ensuring efficient and compliant operations by enabling you to deploy multiple virtual Kubernetes clusters within a single Kubernetes cluster while maintaining a high level of isolation between the clusters.

Platform engineers and architects need a strong knowledge of the challenges and best practices for enhancing multicluster Kubernetes deployments. One such example is understanding the required level of isolation, which can assist engineers in selecting between a multiregion cluster and a virtual cluster. The Multicluster Special Interest Group in Kubernetes is an excellent resource for learning about multicluster tools and best practices.