Raspberry Pi Network Cluster Bottle Necks
When you network many Raspberry Pi 5s, bottlenecks can creep in quickly if you’re not careful.
Here’s how to design the cluster so it scales cleanly:
1. Networking Bottlenecks
Problem: Pis can push a lot of traffic, but if all data funnels through one slow link, the switch becomes the choke point.
Solution:
Use a Gigabit switch (not 100 Mbps). The Pi 5 finally supports full Gigabit Ethernet.
If possible, get a switch with uplink ports (10 GbE or 2.5 GbE) for the backbone.
Keep traffic local inside the cluster using peer-to-peer protocols (MPI, Kubernetes overlay networks).
2. Storage & Disk I/O Bottlenecks
Problem: Each Pi 5 uses microSD or SSD. If all nodes hit storage heavily, SD cards will crawl.
Solution:
Run Pis with USB 3.0 NVMe SSDs for serious workloads.
Use a distributed filesystem (Ceph, GlusterFS, or NFS over the switch).
Cache frequently used datasets in RAM (tmpfs).
3. CPU/GPU Bottlenecks
Problem: A single Pi isn’t powerful enough for ML inference or video workloads.
Solution:
Assign Jetson Orins as the AI accelerators in the cluster.
Use the Pis for coordination, routing, and lighter jobs.
Balance tasks with Kubernetes, k3s, or Docker Swarm.
4. Power & Heat Bottlenecks
Problem: Many Pis = many watts. Heat throttles CPUs.
Solution:
Use PoE (Power over Ethernet) switches + PoE HATs for clean centralized power.
Rack-mount Pis with fans/heat sinks or cluster cases.
Monitor temps with Prometheus + Grafana dashboards.
5. Software / Task Scheduling Bottlenecks
Problem: If jobs aren’t evenly distributed, some Pis sit idle while others choke.
Solution:
Use slurm (job scheduler), Kubernetes, or Ray (Python distributed computing).
Break tasks into small workloads that can be spread across many Pis.
Apply message queues (RabbitMQ, MQTT, or NATS) to balance flows.
6. User Interaction Bottlenecks
Problem: When humans or IoT devices flood the system with requests, it overwhelms nodes.
Solution:
Put an API gateway / load balancer (like Nginx, Traefik, HAProxy) at the entry point.
Use edge AI summarization on Pis before sending data upstream.
In short:
Fast switch + distributed storage + Jetson offload + smart scheduling = no bottlenecks.
When you network many Raspberry Pi 5s, bottlenecks can creep in quickly if you’re not careful.
Here’s how to design the cluster so it scales cleanly:
1. Networking Bottlenecks
Problem: Pis can push a lot of traffic, but if all data funnels through one slow link, the switch becomes the choke point.
Solution:
Use a Gigabit switch (not 100 Mbps). The Pi 5 finally supports full Gigabit Ethernet.
If possible, get a switch with uplink ports (10 GbE or 2.5 GbE) for the backbone.
Keep traffic local inside the cluster using peer-to-peer protocols (MPI, Kubernetes overlay networks).
2. Storage & Disk I/O Bottlenecks
Problem: Each Pi 5 uses microSD or SSD. If all nodes hit storage heavily, SD cards will crawl.
Solution:
Run Pis with USB 3.0 NVMe SSDs for serious workloads.
Use a distributed filesystem (Ceph, GlusterFS, or NFS over the switch).
Cache frequently used datasets in RAM (tmpfs).
3. CPU/GPU Bottlenecks
Problem: A single Pi isn’t powerful enough for ML inference or video workloads.
Solution:
Assign Jetson Orins as the AI accelerators in the cluster.
Use the Pis for coordination, routing, and lighter jobs.
Balance tasks with Kubernetes, k3s, or Docker Swarm.
4. Power & Heat Bottlenecks
Problem: Many Pis = many watts. Heat throttles CPUs.
Solution:
Use PoE (Power over Ethernet) switches + PoE HATs for clean centralized power.
Rack-mount Pis with fans/heat sinks or cluster cases.
Monitor temps with Prometheus + Grafana dashboards.
5. Software / Task Scheduling Bottlenecks
Problem: If jobs aren’t evenly distributed, some Pis sit idle while others choke.
Solution:
Use slurm (job scheduler), Kubernetes, or Ray (Python distributed computing).
Break tasks into small workloads that can be spread across many Pis.
Apply message queues (RabbitMQ, MQTT, or NATS) to balance flows.
6. User Interaction Bottlenecks
Problem: When humans or IoT devices flood the system with requests, it overwhelms nodes.
Solution:
Put an API gateway / load balancer (like Nginx, Traefik, HAProxy) at the entry point.
Use edge AI summarization on Pis before sending data upstream.
In short:Fast switch + distributed storage + Jetson offload + smart scheduling = no bottlenecks.
