K3d Kubernetes exposes SNN workload sensitivity with 47.6x latency spike
Spiking neural networks hit 47.6x latency when CPU limited to 0.5 cores in K3d cluster.
The paper by Huyen Pham and Bilhanan Silverajan evaluates container orchestration for neuromorphic workloads in virtual edge environments using spiking neural networks (SNNs) on a single-node K3d Kubernetes cluster deployed atop Windows 11 with WSL2 and Docker Desktop. The authors measured end-to-end latency, throughput, classification accuracy, infrastructure overhead, and runtime behavior under concurrent load. SNNs, known for event-driven, energy-efficient computation, are tested for feasibility in resource-constrained edge settings.
Key findings: Restricting CPU to 0.5 cores increased median latency by 47.6x and reduced throughput by 49x, with the most constrained configuration failing due to insufficient memory. Classification accuracy remained stable across all working configurations, showing SNN models are robust under resource pressure. However, K3d's default round-robin load balancing caused significant tail latency when scaling replicas, highlighting a mismatch between stateless load-balancing assumptions and the long-running nature of SNN inference. This baseline study underscores the need for careful resource provisioning and orchestration policy tuning, and suggests future work on improved routing, memory optimization, and validation on physical edge hardware.
- SNN latency increased 47.6x and throughput dropped 49x when CPU restricted to 0.5 cores on K3d.
- Classification accuracy remained stable across all working configurations despite resource constraints.
- Default round-robin load balancing in K3d caused significant tail latency under replica scaling for SNN workloads.
Why It Matters
First baseline for deploying neuromorphic SNNs in Kubernetes edge clusters, highlighting critical resource and orchestration pitfalls for energy-efficient AI.