We’ve successfully built and deployed a high-availability (HA) Kubernetes cluster using seven Raspberry Pi nodes. This setup serves as a powerful, Edge-native environment that’s reproducible, scalable, and perfect for testing, learning, or running lightweight production workloads.

In a Nutshell 

• High-Availability: The cluster is designed to stay online even if some nodes fail—minimizing downtime
• Kubernetes Cluster: A system for automating deployment, scaling, and management of containerized applications
• Raspberry Pi Nodes: Inexpensive, low-power single-board computers acting as the hardware for the cluster
• Edge-native: Designed to operate efficiently at the “Edge” (i.e., physically close to data sources like sensors, cameras, etc.), rather than in the Cloud or centralized data centers
• Lightweight Production Workloads: Suitable for applications that aren’t resource-heavy (e.g., small APIs, data processing, etc.)

Cluster Setup

The Raspberry Pi cluster have the following setup:

• 3 Control Plane nodes
• 4 Worker nodes

Provisioning is fully automated using Kubespray, an Ansible-based tool that enables us to manage the cluster as code, ensuring reproducibility and easy scaling.

Key Components and Architecture

Load Balancing with kube-vip + IPVS

• kube-vip provides a virtual IP for Kubernetes API access across control planes
• Combined with IPVS (IP Virtual Server) in Linux kernel mode for efficient and resilient load balancing

Secure and scalable Networking with Cilium

• Using Cilium as the CNI (Container Network Interface)
• Leverages eBPF for:
• High-performance networking
• Deep observability
• Fine-grained policy enforcement

Observability Foundation

• Metrics collection enabled at the cluster level
• Ready for integration with observability stacks like:
• Prometheus
• Grafana
• OpenTelemetry

GitOps-driven Infrastructure

We manage the cluster using a GitOps approach, which ensures:

• Version-controlled configurations
• Automated reconciliation through pull-based controllers
• Auditability and deployment consistency

This minimizes manual changes and supports a modern, scalable DevOps workflow.

Real-Time Failover & Alerting

To enhance site reliability:

• Alertmanager is integrated with Slack webhooks
• Enables immediate human intervention when issues arise
• Complements Kubernetes’ self-healing features
• Improves operational awareness and response time

Why this Project matters

This milestone validates how far we can push affordable hardware (like Raspberry Pi) with modern Kubernetes tooling:

• Edge-native, compact cluster
• Production-grade practices (HA, GitOps, Observability)
• Cost-effective platform for learning and innovation

This environment is ideal for:

• Developers looking to test real-world HA scenarios
• Teams exploring GitOps and observability in a safe sandbox
• Lightweight production workloads at the Edge

Industrial Use Cases

Manufacturing & Industrial Automation

• Edge Analytics for IoT Sensors: Collect and process machine data (vibration, temperature, pressure) locally to detect anomalies
• Factory Floor Automation: Run containerized apps for robot control, quality inspection, or predictive maintenance—minimizing Cloud dependency
• SCADA Integration: Host lightweight SCADA (Supervisory Control and Data Acquisition) components at the Edge to improve response times and system resilience

Logistics & Transportation

• Fleet Edge Nodes: Place clusters in distribution centers to monitor goods movement, optimize routing, and reduce latency
• Cold Chain Monitoring: Edge processing for temperature/humidity sensors on perishable goods to ensure compliance
• Autonomous Vehicle Support: Use Edge clusters at depots for uploading sensor logs or syncing navigation data from trucks/drones

Agriculture

• Smart Farming Gateways: Deploy Edge services to aggregate data from soil sensors, weather stations, and drones
• Autonomous Irrigation Control: Run Kubernetes-based control systems for water distribution based on sensor input and rules
• Local Data Aggregation & Forecasting: Enable farming co-ops to collect data across fields and forecast crop needs without needing internet access

Education & Research

• University Labs: Provide hands-on Kubernetes learning environments for computer science or engineering students
• STEM Outreach Projects: Power affordable, portable clusters for workshops or tech education in underserved communities
• Research Field Stations: Deploy clusters at wildlife reserves or geological sites to collect and process research data on-site

Healthcare

• Local Health Monitoring Systems: Collect and process patient vitals or wearable device data in rural or disconnected clinics
• Edge Imaging Processing: Run lightweight diagnostic tools on ultrasound or X-ray images closer to the point of care
• Mobile Medical Units: Power field-deployable healthcare setups in disaster zones or remote villages

Utilities & Energy

• Grid Monitoring at Substations: Analyze grid health data locally and respond to events faster than a Cloud-dependent system
• Remote Site Monitoring: Manage pipelines, solar farms, or wind turbines in disconnected environments
• Smart Meter Aggregation: Edge collection and pre-processing of smart meter data before Cloud sync

Retail

• In-Store Smart Systems: Host POS systems, customer behavior analytics, or digital signage management locally for reliability
• Inventory Management: Run vision-based product recognition and stock monitoring systems without Cloud reliance
• Pop-Up Retail Tech: Use in temporary locations where traditional infrastructure is unavailable or too expensive

What’s next?

We plan to:

• Add CI/CD pipelines for application deployment
• Expand observability with full Prometheus + Grafana + OpenTelemetry integration
• Migrate to reproducible environments with Virtualization and IaC (Infrastructure as Code) with Terraform
• Explore multi-cluster federation scenarios

Wrapping Up

This project shows that high-availability Kubernetes clusters aren’t just reserved for Cloud giants or enterprise data centers — they can be built at the Edge using affordable, low-power hardware like Raspberry Pi. Whether you're in manufacturing, research, or just passionate about infrastructure, this setup opens doors to scalable, resilient deployments closer to where data is created.

As we continue building on this foundation with CI/CD, observability, IaC, and federation, we’re excited to explore what’s possible at the Edge. Stay tuned — and if you’re experimenting with similar setups, we’d love to hear your thoughts or challenges.

Project Credentials

This initiative is part of a broader strategic effort supported under the IPCEI-CIS (Important Project of Common European Interest on Next Generation Cloud Infrastructure and Services). The project is co-funded by the European Union and national governments, reinforcing Europe's technological sovereignty and commitment to cutting-edge, sustainable digital infrastructure.

Our contribution aligns with the goals of IPCEI-CIS by demonstrating how affordable, energy-efficient Edge computing can enable next-generation cloud-native services—especially in decentralized or resource-constrained environments.

Learn more about the broader initiative:

• EU Project Overview: IPCEI-CIS at the European Commission
• German National Context: Federal Ministry for Economic Affairs and Climate Action (BMWK)