General discussion • Optimizing Raspberry Pi Cluster Performance for Parallel Data Processing with Kubernetes

Hi everyone,

I am currently working on a Raspberry Pi 4 cluster (4GB RAM each) for parallel data processing applications using Kubernetes. My goal is to optimize performance in terms ·
of load distribution and CPU/RAM resource management, particularly for lightweight scientific computations and real-time data stream analysis.

After setting up my cluster with K3s for its lightweight nature, I have encountered several technical challenges related to:

• Optimizing the CNI (Container Network Interface) to minimize network latency and prevent master node overload.
  Choosing the right QoS (Quality of Service) ·
  settings and CPU requests/limits to avoid throttling and maximize performance without saturation.
• Managing persistent volumes over network storage (NFS vs Ceph) to balance access latency and resilience.

I have already explored several solutions, such as enabling cgroups v2 for better resource allocation control and implementing a custom scheduler, but I wonder if any of you have experience with:

Specific kubelet configurations to optimize pod management on ARM architectures.
Load balancing strategies tailored for Raspberry ·
Pi clusters to avoid bottlenecks on intensive workloads.
The impact of mild CPU overclocking on stability in production environments.
Thanks in advance for your insights and suggestions!

Statistics: Posted by spidercoco — Sun Jan 19, 2025 3:48 pm

---