The growing adoption of edge computing has created an increasing need for workloads capable of operating under strict resource and energy constraints. Neuromorphic computing, and spiking neural networks (SNNs) in particular, offers an energy-efficient alternative to conventional machine learning through event-driven computation. However, how SNN workloads behave when deployed within modern container orchestration frameworks, especially in edge environments, remains largely unexplored. This paper investigates the feasibility of deploying and orchestrating SNN workloads in a virtual edge environment using Kubernetes, focusing on end-to-end latency, throughput, classification accuracy, infrastructure overhead, and runtime behavior under concurrent load. Experiments were conducted on a single-node K3d cluster running on a Windows 11 host with WSL2 and Docker Desktop. The results show that SNN workloads are highly sensitive to resource availability. Restricting CPU to 0.5 cores increased median latency by 47.6x and reduced throughput by 49x, while the most constrained configuration failed due to insufficient memory. Classification accuracy remained stable across all working configurations. From an orchestration perspective, K3d successfully deployed and scaled SNN workloads, though its default round-robin routing policy introduced significant tail latency under replica scaling, highlighting a mismatch between stateless load-balancing assumptions and long-running inference workloads. Overall, this study provides a baseline for deploying neuromorphic workloads in containerized edge environments and highlights the importance of resource provisioning and orchestration configuration. Future work should explore improved routing strategies, memory optimization, and validation on physical edge hardware.

翻译：边缘计算的日益普及催生了在严格资源与能量约束下运行工作负载的需求。神经形态计算，特别是脉冲神经网络（SNN），通过事件驱动计算提供了传统机器学习的能效替代方案。然而，SNN工作负载在现代容器编排框架（尤其在边缘环境中）的表现仍鲜有研究。本文探究了在虚拟边缘环境中使用Kubernetes部署和编排SNN工作负载的可行性，重点关注端到端延迟、吞吐量、分类精度、基础设施开销以及并发负载下的运行时行为。实验基于运行在Windows 11主机（配置WSL2与Docker Desktop）上的单节点K3d集群完成。结果表明，SNN工作负载对资源可用性高度敏感：将CPU限制为0.5核导致中位延迟增加47.6倍、吞吐量降低49倍，而最受限配置因内存不足而失败。所有可行配置下的分类精度保持稳定。从编排角度看，K3d成功部署并扩展了SNN工作负载，但其默认的轮询路由策略在副本扩展时引入显著尾延迟，揭示了无状态负载均衡假设与长时间推理工作负载之间的不匹配。总体而言，本研究为在容器化边缘环境中部署神经形态工作负载提供了基准，并强调了资源供应与编排配置的重要性。未来工作应探索改进的路由策略、内存优化及在物理边缘硬件上的验证。