Colibri: Efficient Collection of Fine-Grained Resource Metrics Necessary for Mobile Edge Computing

Effective provisioning and resource management in edge environments are critical for ensuring infrastructure efficiency while providing latency-critical service-level objectives (SLOs). Realizing this requires low-overhead aggregation of fine-grained information regarding workloads' resource demands. Unfortunately, we show that this cannot be adequately achieved with existing solutions used for cloud technologies such as Kubernetes and containers, which are prevalent in edge systems. We propose Colibri, a lightweight and flexible monitoring system for edge computing that characterizes containers across CPU, memory, and network resource usage patterns at millisecond granularity. Colibri can be dispatched dynamically as needed and enables accurate characterization of workload resource usage. We demonstrate experimentally that using Colibri can significantly reduce SLO violations caused when relying on existing tools, while saving resources for representative edge workloads. Colibri provides this while consuming only 2% of the resources used by existing cloud monitoring tools when they operate at the same query granularity, making it an efficient and effective solution for edge computing environments.