Poster: Fine-grained Control Plane Container Profiler for MEC

Abstract

Today, the edge computing system stack is built by leveraging the current cloud technologies, such as the containers, Kubernetes, etc., because, like the cloud, the edge is multi-tenant infrastructure. However, edge applications have more latency-critical SLAs and the infrastructure itself resource-constrained. That puts additional burdens on its control plane, which are not addressed by the cloud control plain tools. At the edge, if deployments aren't specified accurately, edge providers will face the dilemma between the waste of resource due to overcommitment vs. SLA violations. However, we observed that it is not feasible to rely on the existing monitoring tools, designed for the cloud, to glean that information from workloads with varying use of resources, at the needed fine granularity. Trying to do that with brute-forcing cloud solutions turns out to be extremely demanding on the resources allocated to the control plane. We present a new control plane tool, Colibri, aimed at addressing those conflicting requirements. Colibri can be dispatched dynamically, when needed, and enables characterization of containers deployed using Kubernetes across CPU, memory and network resource usage patterns at millisecond scale. The preliminary results demonstrate the effectiveness of out approach in reducing SLA violations by up to 98% for representative edge workloads.