The Complete Guide to the Kubernetes Control Plane

Kubernetes is a leading standard in the world of container orchestration. At the center of this powerful platform lies the Kubernetes control plane, which is responsible for orchestrating and regulating the entire Kubernetes cluster.

The Kubernetes control plane is a set of components that collectively make decisions about the state of the cluster. It manages the overall cluster and its nodes, ensuring that the desired state—defined by the user or applications—is maintained. The control plane components communicate with one another and with nodes to orchestrate and coordinate the deployment, scaling, and operation of applications within the Kubernetes cluster.

Here’s how the control plane fits in a Kubernetes cluster:

Image courtesy of kubernetes.io

In this article, you’ll learn more about the Kubernetes control plane, its components, requirements, and other details. You’ll also learn how control planes work in virtual clusters, a fast-growing Kubernetes cluster virtualization technology.

#Core Components of the Kubernetes Control Plane

The Kubernetes control plane is the brain behind a Kubernetes cluster, and it’s responsible for managing the cluster’s state, coordinating operations, and maintaining overall cluster health. There are several components that make up the control plane. These include:

• API server: This is the central component of the control plane that exposes the Kubernetes API. It acts as the entry point for all operational commands and management tasks.
• etcd: This consistent and highly available key-value store is used as the cluster’s primary database. It stores configuration data, state information, and metadata about the cluster.
• Controller manager: This watches the shared state of the cluster through the API server. Also, it ensures that the current state matches the desired state and manages various controllers for nodes, endpoints, replication, and more.
• Scheduler: The scheduler is responsible for assigning nodes to newly created pods based on resource availability and constraints. It also monitors the resource usage of individual nodes and makes decisions about where to deploy new pods.
• Cloud controller manager: This is an optional component of the Kubernetes control plane. It integrates with the cloud provider APIs to manage resources specific to a particular cloud platform, such as load balancers, storage volumes, and networking.

#Hardware and Resource Requirements

The minimum hardware and resource specifications needed to run a functional Kubernetes control plane can vary based on factors such as the size of the cluster, the number of nodes, and the overall workload. Below are general guidelines for a small-scale or development environment:

Master node (control plane node):

• CPU: 2 cores or more
• Memory: 4 GB RAM or more
• Disk: 20 GB or more (for the operating system, logs, and other system components)

etcd (cluster store):

• CPU: 1 core or more
• Memory: 2 GB RAM or more
• Disk: 10 GB or more (for etcd data storage)

These are basic recommendations and may be suitable for learning purposes, development environments, or small clusters. In a production setting or for larger clusters, you would need to scale up these resources based on factors such as the number of nodes and pods as well as the complexity of your workload.

#Scalability Considerations

While the Kubernetes documentation does not specify a minimum requirement for hardware resources, it does provide clear configuration guidelines for optimal resource management in large clusters, which are:

• No more than 110 pods per node
• No more than 5,000 nodes
• No more than 150,000 total pods
• No more than 300,000 total containers

You should make sure that your cluster’s configuration follows these guidelines for optimal resource usage and performance when scaling up.

#How to Make the Kubernetes Control Plane Highly Available

Ensuring the high availability of the Kubernetes control plane is essential for maintaining a robust and reliable container orchestration platform.

High availability minimizes downtime and ensures the continuous operation of critical Kubernetes control plane components. A highly available control plane contributes to the overall reliability of the cluster, allowing it to minimize the effects of hardware failures, network issues, or other unforeseen challenges.

#Use Replication and Redundancy

The primary strategy for achieving high availability is through replication and redundancy. Key control plane components should be replicated across multiple nodes to mitigate the impact of failures. By doing so, the cluster can continue functioning even if individual nodes or components experience issues.

#Use Strategies for High Availability

Some of the key strategies for achieving high availability for your Kubernetes control plane include:

• Multiple control plane nodes with external etcd: In this strategy, you deploy multiple master nodes, each running a subset of control plane components (API server, controller manager, and scheduler). The etcd data store runs separately from the control planes. Requests are distributed among the control plane nodes through a load balancer while they all communicate with the external etcd data store to save the cluster’s state.
• Stacked control plane nodes: In this strategy, you deploy multiple control plane nodes that run a full set of control plane components. Each node has its own data store as well, and the Raft consensus algorithm is used for leader election and handling requests that need cluster consensus.

#Use Kubernetes Native Solutions for High Availability

Kubernetes provides native features and tools to support high availability in the control plane:

• kube-apiserver replicas: You can run multiple instances of kube-apiserver (the Kubernetes API server) and distribute them behind a load balancer. This ensures that API requests are evenly distributed and increases the fault tolerance of the API server.
• Highly available etcd cluster: You can set up an etcd cluster with multiple nodes (three or more) to prevent a single point of failure. You can also distribute etcd nodes across different physical or virtual machines for enhanced redundancy.
• Control plane node replication: You can leverage tools like kubeadm or other Kubernetes deployment solutions that support control plane node replication. These tools automate the process of deploying and replicating control plane components.

#What Is the Difference between the Master Node and Control Plane in Kubernetes?

In Kubernetes, the terms “master node” and “control plane” are often used interchangeably to refer to the set of components that collectively manage the cluster. However, it’s important to understand that “control plane” is a more comprehensive and accurate term that has evolved from “master node.”

#Historical Use of “Master Node”

In the early days of Kubernetes, the term “master node” was commonly used to refer to a node in the cluster that hosted the control plane components. The control plane components included the API server, controller manager, scheduler, and, in some cases, the etcd data store. The master node played a central role in managing the cluster’s state, receiving and processing API requests, and making decisions about resource allocation.

While the term “master node” accurately described the role of the node hosting the control plane components, it had some limitations and potential for misunderstanding:

• Implication of a hierarchy: The term “master” can imply a hierarchical structure where one node is more authoritative or superior to others. In reality, Kubernetes aims for a more decentralized and distributed architecture.
• Single point of failure: Referring to a node as a “master” may suggest a single point of failure. In practice, Kubernetes deployments aim for high availability by deploying multiple nodes with control plane components.

#Evolution to “Control Plane”

As the understanding of Kubernetes architecture matured, the industry shifted towards using the term “control plane” to describe the collective set of components responsible for managing the cluster. This change in terminology reflects a more accurate and holistic view of the system. The control plane comprises all components that make decisions about the state of the cluster and ensure that the desired state is maintained.

The control plane includes, but is not limited to, the components traditionally hosted on a “master node.” This shift in terminology acknowledges the distributed and decentralized nature of Kubernetes, where multiple nodes can host control plane components for fault tolerance and scalability.

The key components of a control plane, as discussed earlier, include the API server, the etcd data store, the controller manager, the scheduler, and an optional cloud controller manager.

#How Many Kubernetes Control Planes Should You Have?

The number of Kubernetes control planes you should have depends on factors such as your deployment goals, your desired level of availability, and the size and criticality of your workloads. The following are some considerations.

#Single vs. Multiple Control Plane Setups

The single control plane setup is the most basic setup of all. A single control plane in Kubernetes refers to a deployment architecture where there is only one node responsible for hosting all the essential components of the control plane. This is straightforward to set up and manage, and it typically requires fewer resources than a setup with multiple masters.

Here are some of its use cases, advantages, and considerations:

• Use case: Suitable for development, testing, and small-scale deployments.
• Advantages: Simplicity, lower resource requirements, easier management for small projects.
• Considerations: Single point of failure, limited fault tolerance.

A multiple control plane setup, on the other hand, refers to a deployment architecture where there are multiple control plane nodes, each hosting a full set of control plane components. This setup is designed to enhance the high availability and fault tolerance of the Kubernetes control plane.

In a multiple control plane configuration, if one control plane instance becomes unavailable or experiences issues, the remaining instances can continue to manage and control the cluster, ensuring continuous operation.

Here are some of its use cases, advantages, and considerations:

• Use case: Recommended for production environments where high availability is critical.
• Advantages: Improved fault tolerance, redundancy, and load distribution.
• Considerations: Requires additional resources, careful network planning, and load balancing.

Such an architecture gives you the freedom to host and maintain dedicated control planes for various environments, cloud providers, and business units. If the nature of your business requires data or control to be segregated across regions or business teams, a multiple control plane approach can help you achieve compliance. You can set up a multitenant environment easily with multiple control planes and allow teams to own their control planes and the associated resources.

Deploying a multiple control plane setup in Kubernetes brings numerous benefits in terms of availability and fault tolerance, but it also introduces additional complexity:

• Load balancing configuration: Configuring and maintaining load balancing solutions for distributing API requests across multiple control planes introduces complexity. So, it’s essential that you choose a load balancing strategy that suits the deployment environment.
• TLS and security: Implementing secure communication between components becomes more complex with multiple control planes. Managing TLS certificates and maintaining security best practices across control planes require careful attention.
• High availability protocols: Deploying highly available configurations often involves implementing protocols and technologies like virtual IP (VIP) or clustering solutions. These technologies add complexity to the infrastructure.
• Monitoring and observability: Monitoring a multiple control plane setup requires a comprehensive strategy. Tools and practices for monitoring the health and performance of each control plane component are essential.

There are some ways to mitigate potential challenges with multiple control plane setups:

• Network resilience: Implement a robust load balancing strategy to evenly distribute API requests among instances. Make sure to properly segment the network to isolate and control plane traffic and enhance security.
• Security best practices: Enforce TLS encryption for communication between control plane components. Also, follow security best practices for securing etcd and other control plane components.
• Operational excellence: Utilize automated tools for deployment, scaling, and maintenance to reduce operational overhead. You should also implement comprehensive monitoring and alerting to detect and respond to issues promptly. Finally, develop detailed runbooks for common operational tasks, including upgrades, patching, and troubleshooting.

#Hybrid Approaches

You could also consider using a hybrid approach by using a mixed single and multiple control plane setup. This would involve deploying a combination of clusters with different control plane configurations based on specific requirements and workloads. This hybrid approach allows organizations to tailor their Kubernetes architecture to achieve the right balance between simplicity and high availability.

Some of the key characteristics of a hybrid approach include:

• Workload-specific deployments: You can deploy single control plane clusters for less critical workloads or development environments where high availability is not a top priority. You can use multiple control plane clusters for mission-critical workloads demanding higher fault tolerance.
• Resource optimization: You can optimize resource allocation by choosing the control plane configuration that best suits the characteristics of the workloads. Single control plane clusters may be more resource-efficient for certain use cases, while multiple-master clusters offer redundancy and resilience at a slightly premium cost.
• Simplicity for development and testing: You can utilize single control plane clusters for development and testing scenarios where the emphasis is on simplicity with flexibility. Developers may find it easier to work with a single control plane during the development phase.
• High availability for production: You can reserve multiple control plane clusters for production environments where high availability and fault tolerance are critical. This ensures that critical applications can withstand the failure of a control plane without disrupting operations.

You can use any of the three approaches listed above according to your cluster’s requirements. These are some general guidelines that may also come in handy:

• For small-scale or non-production environments, a single control plane may be sufficient.
• In production, especially for critical workloads, a multiple control plane setup is often recommended for improved fault tolerance.
• For large-scale and globally distributed deployments, consider a multiple control plane setup with multiple nodes distributed across regions or a hybrid approach with a mix of single and multiple master control planes.

#Managing Control Planes for Virtual Clusters

If you have worked with Kubernetes namespaces, you’ve probably come across virtual clusters. Virtual clusters are often seen as an intelligent alternative to namespaces that allow you to create fully working Kubernetes clusters on top of other Kubernetes clusters. These virtual clusters reuse worker nodes and the network of the host cluster, but they have their own control plane.

You should use virtual clusters in your Kubernetes infrastructure if you are looking to partition a single physical Kubernetes cluster into multiple smaller clusters while retaining the core benefits of Kubernetes, such as optimal resource distribution and workload management.

When it comes to control planes and virtual clusters, you already know that each virtual cluster gets its own control plane, thus ensuring better isolation between workloads and better rate limiting in the virtual cluster as well.

With Loft, you can easily manage all your virtual clusters. Moreover, you can utilize Loft’s sleep mode to put virtual clusters to sleep when they are not in use, giving you centralized control over your Kubernetes infrastructure.

#Conclusion

The Kubernetes control plane is the orchestrator of a cluster. It manages the overall state and ensures the seamless functioning of workloads. This article explored the details of the control plane, from its fundamental components like the API server, etcd, and controllers to deployment architectures such as single control plane, multiple control plane, and highly available configurations.

You also explored considerations for fault tolerance, scalability, and security. The hybrid approaches, combining single and multiple master control planes, offer a nuanced strategy for optimizing resources and meeting diverse workload requirements. If you understand these concepts, you’ll be well-equipped to build robust, scalable, and resilient container orchestration environments with Kubernetes.