Kubernetes cluster architecture separates the Control Plane (left, dashed box) from Worker Node machines (right). The control plane components (API server, scheduler, controller manager, etcd) orchestrate the cluster state, while each worker node runs a kubelet and kube-proxy to host application Pods and handle networking.

As a DevOps engineer, it’s crucial to understand Kubernetes’ layered architecture. A Kubernetes cluster is divided into a Control Plane (master components) and Worker Nodes (the servers that run your containers). Each layer comprises specific components that play distinct roles in managing and operating the cluster. Below is a concise overview of the key layers and components in Kubernetes, along with their functions and relevance to DevOps practices:

• Control Plane (Master Layer): The control plane is the “brain” of the cluster, managing the overall state and making global decisions. It includes components like the API Server, Scheduler, Controller Manager, and etcd. The API server exposes the Kubernetes API and handles all cluster requests; the scheduler assigns Pods to appropriate nodes; the controller manager runs controllers to maintain desired state (e.g. ensuring the correct number of pod replicas); and etcd is the cluster’s backing datastore for all config and state. A healthy control plane is critical – DevOps engineers must ensure it’s highly available and secured, since it orchestrates everything in the cluster (from scheduling workloads to responding to node or Pod failures).
• Worker Nodes (Data Plane): Worker nodes are the machines (VMs or physical servers) that actually run the containerized applications. Each node runs several key components: a kubelet (agent) to communicate with the control plane and ensure containers in Pods are running as expected, a Container Runtime (such as containerd or CRI-O) to launch and manage containers, and usually a kube-proxy for networking. The kube-proxy on each node sets up networking rules to route traffic for Kubernetes Services, enabling pods to reach each other and the outside world. For DevOps, the node layer is where you monitor resource usage (CPU/memory), scale out by adding/removing nodes, and perform maintenance. Ensuring kubelet and the container runtime are updated and the nodes are properly configured (labels, taints, etc.) is part of day-to-day cluster operations.
• Networking Layer (Service & Network Connectivity): Kubernetes abstracts networking so that any Pod can communicate with any other Pod or Service by IP, regardless of which node they’re on. Services provide a stable virtual IP and DNS name to group pods, managing network traffic and load-balancing across them. Inside each node, kube-proxy implements the Service networking by maintaining iptables rules or IPVS routes to direct traffic to the correct pod endpoints. Kubernetes also relies on a Container Network Interface (CNI) plugin (e.g. Calico, Flannel) to provision pod networking across nodes – this ensures every pod gets an IP address and enables cross-node networking. For DevOps engineers, understanding the networking layer is vital for troubleshooting connectivity (e.g. why two pods can’t talk, or debugging an ingress issue) and configuring network policies or Ingress controllers to securely expose services to external users.
• Storage Layer (Persistent Data): By default, containers and pods are ephemeral – if they restart or move, any local data is lost. Kubernetes provides the Persistent Volume (PV) framework to handle stateful needs. A PV is a piece of storage (e.g. an AWS EBS volume, NFS share, etc.) that is provisioned to the cluster, and pods can claim it via Persistent Volume Claims. This decouples storage from the pod lifecycle, allowing data to persist even if pods are rescheduled. Modern clusters use the Container Storage Interface (CSI) with storage classes to dynamically provision volumes on-demand. In practice, DevOps engineers must ensure the right storage classes are configured (for SSD vs HDD, network storage, etc.) and that backups/retention policies are in place for critical data. The storage layer is key for running databases or any stateful services on Kubernetes, as it provides the durability that containers alone do not.

Each of these layers works in unison to deliver a resilient, scalable platform. Understanding these Kubernetes layers helps DevOps practitioners effectively deploy, monitor, and troubleshoot applications on the cluster. By knowing what each component does and how the layers interact, you can better tune the cluster’s behavior and quickly diagnose issues — ensuring your cloud-native applications run smoothly on Kubernetes.

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