Evaluating Kubernetes at the Edge for Fault Tolerant Multi-Camera Computer Vision Applications

The rise of AI-powered computer vision algorithms offers the possibility of visual sensing of the environment in IoT applications through the widespread use of low-cost video cameras. The need for low latency, bandwidth limitations, and privacy concerns associated with video data motivates the use of edge computing for computer vision applications. However, unlike cloud computing with almost unbounded resources, the edge is characterized by compute nodes of limited capacity and power budget. Additionally, fault tolerance is limited due to replication costs at the edge.In this poster, we present our initial work on evaluating the performance of an edge-specific version of Kubernetes on a Raspberry Pi4 cluster for multi-camera computer vision applications. Kubernetes enables automated deployment and management of containerized distributed applications to run at scale across a cluster of compute and storage nodes, while handling node failures. However, existing literature has not characterized the resource consumption and latency impact of Kubernetes for computer vision applications on realistic edge clusters. Our experimental results indicate that while Kubernetes can deliver fault tolerance at the edge, the choices made in the design of containers pods significantly affects the observed tail latency on a low power edge cluster.