K3s Raspberry Pi Cluster Orchestration

The integration of K3s with Raspberry Pi hardware represents a paradigm shift in the accessibility of container orchestration. K3s, a lightweight Kubernetes distribution engineered by Rancher (which has since become part of SUSE), is specifically designed to bring the power of Kubernetes to resource-constrained environments. By stripping away legacy cloud-provider specific code and non-essential features that are typically only utilized in massive hyperscale cloud clusters, K3s provides a fully functional, production-ready Kubernetes environment that can operate on single-board computers. This synthesis allows users to transform a collection of credit-card-sized devices into a robust, distributed computing system.

The utility of this combination extends across various user profiles. For the tech enthusiast or student, it serves as a low-cost gateway to learning the complexities of Kubernetes, providing a tangible way to build real clusters using industry-standard tools without incurring the recurring costs of cloud providers. For the edge computing professional, it offers a portable and scalable architecture that can be deployed in diverse locations, from a home desk to a remote shed or an industrial edge site. Furthermore, the ability to run real workloads—including databases, monitoring dashboards, and custom applications—on devices with as little as 1GB to 4GB of RAM demonstrates the efficiency of the K3s architecture.

A Kubernetes homelab utilizing Raspberry Pi 5 devices allows for the practical application of infrastructure management and automation. By hosting services such as Home Assistant for smart home orchestration or Pi-hole for network-wide ad-blocking, users can experience the benefits of self-healing infrastructure. In a Kubernetes environment, if a pod fails or a hardware node crashes, the orchestrator automatically reschedules the workload onto the remaining active nodes, ensuring high availability for critical home services.

Hardware Architecture and Resource Requirements

Deploying K3s requires a strategic approach to hardware selection to ensure cluster stability and performance. While the software is lightweight, the physical limitations of single-board computers necessitate specific hardware choices.

Single-Node Minimum and Recommended Specifications

For those starting with a single-node setup, the baseline requirements ensure that the K3s control plane and agent can coexist without exhausting system resources.

The choice of RAM is critical; while 2GB is the absolute minimum, 4GB or 8GB provides the necessary breathing room for deploying actual workloads. Storage is another critical failure point; while a 16GB microSD card can boot the system, an A2-rated 32GB card or a USB-connected SSD is strongly recommended to handle the increased I/O demands of containerized applications.

Raspberry Pi 5 High-Performance Configuration

The Raspberry Pi 5 acts as a powerhouse for K3s, offering significantly improved compute capabilities. For a professional or heavy-duty homelab, the following prerequisites are advised:

• Hardware: A Raspberry Pi 5 equipped with at least 4GB of RAM, though 8GB is recommended for larger workloads.
• Storage: High-quality microSD cards (32GB or larger) or, preferably, an SSD via USB for booting.
• Power and Network: A stable power supply and a consistent network connection.
• Software: Raspberry Pi OS (64-bit recommended), fully installed and updated.
• Access: SSH access or direct terminal access to the device.

Multi-Node Cluster Topology

To move beyond a single-node setup and achieve true orchestration, a multi-node cluster is required. A proper cluster experience typically involves at least three nodes to distribute the control plane and workload.

• Master Node: A Raspberry Pi 4 with 4GB of RAM acting as the Control Plane.
• Worker Node 1: A Raspberry Pi 4 with 4GB of RAM acting as a worker node.
• Worker Node 2: A Raspberry Pi 4 with 4GB of RAM acting as a worker node.

In this architecture, the Master node coordinates the cluster, while the worker nodes execute the pods via the K3s Agent.

Cluster Shopping List for Three-Node Deployment

Building a three-node cluster requires a precise list of components to avoid system instability.

• Raspberry Pi 4 (4GB recommended) x3: 4GB provides sufficient memory for general workloads, while 8GB is advised for memory-intensive applications like databases.
• microSD cards (32GB A2 rated) x3: A2 rating is essential for faster random I/O performance. For production-grade workloads, SSD boot is the superior choice.
• USB-C power supplies (5V 3A) x3: Using underpowered supplies can lead to random system crashes. Only official or certified PSUs should be used.
• Ethernet cables (Cat6) x3: Gigabit Ethernet is essential for maintaining low-latency cluster traffic. WiFi is strongly discouraged in cluster environments.

Storage and I/O Optimization

Storage is the most common bottleneck in Raspberry Pi clusters. The K3s distribution utilizes etcd for state management, which is highly write-intensive.

The SSD Imperative

Because etcd performs frequent write operations, standard microSD cards and eMMC storage often struggle to handle the I/O load. This can lead to performance degradation or premature card failure. To ensure optimal speed and longevity, the use of an external SSD is strongly recommended. This transition from SD cards to NVMe or SATA SSDs via USB 3.0 significantly improves the stability of the control plane.

Server Sizing and Scalability

The capacity of a K3s cluster is directly tied to the resources available on the server (control-plane + etcd) node. When CPU and RAM are limited, there is a hard cap on the number of agent nodes that can be joined.

Control Plane Resource Mapping

The following table outlines the scalability of a K3s cluster based on server hardware:

For environments requiring higher scalability, a high-availability (HA) setup with three server nodes can increase the agent capacity by roughly 50%. For example, a 3-server configuration with 4 vCPU and 8GB RAM can scale to approximately 1200 agents.

Node Joining Strategy

To maintain system stability during cluster expansion, agent nodes should be joined in batches of 50 or fewer. This prevents CPU spikes during the join process and allows the server to manage the new load efficiently. Additionally, if a user intends to scale beyond 255 nodes, the default cluster-cidr must be modified.

Networking and Connectivity

Cluster communication relies on specific ports being open and accessible across all nodes.

Port Requirements

• Server Access: The K3s server must have port 6443 accessible to all nodes in the cluster.
• Flannel VXLAN: Nodes must be able to reach each other over UDP port 8472 when using the Flannel VXLAN backend.
• Flannel WireGuard: Nodes must use UDP port 51820 (and 51821 for IPv6) when utilizing the Flannel WireGuard backend.

The nodes should not listen on any other ports unless specifically required by the deployed applications.

Step-by-Step Installation on Raspberry Pi 5

The installation process involves preparing the OS, configuring the kernel for Kubernetes, and executing the K3s installation script.

Step 1: System Preparation and Updates

Before installing K3s, the underlying Raspberry Pi OS must be fully updated to ensure package compatibility.

• Update the system packages:
  sudo apt update -y

• Install necessary network tools:
  sudo apt install -y curl wget

Step 2: Enabling Cgroup Support

Kubernetes requires cgroup support to manage resource allocation for containers. This is not enabled by default on Raspberry Pi OS and must be configured manually in the boot configuration.

• Edit the boot command line file:
  sudo nano /boot/firmware/cmdline.txt

• Append the following parameters to the end of the existing line, ensuring no new lines are created:
  cgroup_memory=1 cgroup_enable=memory

• Save the file and reboot the device to apply the kernel changes:
  sudo reboot

Step 3: K3s Installation

Once the system has rebooted and cgroups are enabled, the K3s installation can be performed using the official installation script.

• Execute the installation script:
  curl -sfL https://get.k3s.io | sh -

By default, this command installs K3s as a server, establishing a single-node Kubernetes cluster.

• Verify the status of the K3s service:
  sudo systemctl status k3s

Step 4: Cluster Configuration and Verification

After the installation is complete, the cluster status can be verified using kubectl, which is installed by default with K3s.

• Check the status of nodes:
  sudo kubectl get nodes

The output should show the Raspberry Pi node in a Ready state.

Step 5: Expanding the Cluster (Adding Worker Nodes)

To convert a single-node setup into a multi-node cluster, additional Raspberry Pi devices must be joined as workers.

• Retrieve the node token from the server node:
  sudo cat /var/lib/rancher/k3s/server/node-token

• Run the join command on the worker node, replacing <SERVER_IP> and <NODE_TOKEN> with the actual values:
  curl -sfL https://get.k3s.io | K3S_URL=https://<SERVER_IP>:6443 K3S_TOKEN=<NODE_TOKEN> sh -

Application Deployment and Orchestration

With the cluster operational, users can begin deploying containerized applications. The power of K3s on Raspberry Pi lies in the ability to automate the deployment of services that can scale and recover automatically.

• Home Automation: Deploying Home Assistant allows for centralized control of smart devices.
• Network Management: Deploying Pi-hole enables network-wide ad-blocking.
• Custom Workloads: Users can deploy databases, dashboards, and custom microservices.

If a pod fails on one node, Kubernetes automatically reschedules it on another available node, provided the cluster has sufficient resources. This self-healing property makes the Raspberry Pi cluster a viable option for services that require high uptime in a home or edge environment.

Detailed Analysis of K3s vs. Standard Kubernetes

The transition from standard Kubernetes to K3s on ARM-based hardware involves a significant reduction in overhead. Standard Kubernetes is designed for massive cloud environments and includes a vast array of cloud-provider integrations that are redundant for a Raspberry Pi cluster. K3s replaces these with a streamlined architecture.

The impact of this reduction is most evident in memory usage. K3s can run on devices with 1GB to 4GB of RAM, whereas a full Kubernetes installation would struggle to maintain basic stability on such limited hardware. This allows for a "desk-to-edge" scalability model where the same configuration used in a small home lab can be scaled to larger edge deployments.

From an infrastructure perspective, the use of a single binary for the K3s server simplifies the deployment process. The integration of the Flannel CNI (Container Network Interface) allows for flexible networking options, whether using VXLAN or WireGuard. The choice between these backends depends on the security and performance requirements of the cluster, with WireGuard providing encrypted tunnels for traffic between nodes.

The physical layout of the cluster, typically connected via a Gigabit Ethernet switch to a home router, ensures that the internal cluster traffic (the "East-West" traffic) remains fast and does not saturate the primary internet uplink. This is why Gigabit Ethernet is strongly preferred over WiFi, as the latter introduces latency and instability that can cause node heartbeat failures, leading the master node to mark workers as NotReady.

Sources

1. KevsRobots Learning Platform
2. OneUptime
3. PicoCluster
4. Dakaiser Substack
5. K3s Documentation