An End-to-End Framework for Benchmarking Edge-Cloud Cluster Management Techniques

Abstract

This paper presents a framework for defining, performing, and analyzing distributed load testing experiments for benchmarking edge-cloud clusters. This end-to-end workflow helps researchers build reproducible environments to evaluate cluster management techniques. Our implementation extends the open source tool Galileo by adding support for distributed execution on Kubernetes clusters, additional system monitoring instruments, as well as out-of-the box experiment workloads. We focus on providing tools that run across popular CPU architectures and provide a set of representative workloads, such as edge AI functions. We demonstrate our framework's capabilities in a set of experiments based on use cases commonly found in edge computing systems research. Additionally, we show that the resource usage of our system is minimal and that it can run on resource-constrained devices.