MECBench: A Framework for Benchmarking Multi-Edge Computing Systems

Abstract

I present MECBench, an extensible benchmarking framework for multi-access edge computing. MECBench is configurable and can emulate networks with different capabilities and conditions, can scale the generated workloads to mimic large number of clients, and can generate a range of workload patterns. MECBench is extensible; it can be extended to change the generated workload, use new datasets, and integrate new applications. MECBench’s implementation includes machine learning and synthetic edge applications. I demonstrate MECBench’s capabilities through three scenarios: an object detection processing for drone navigation, a natural language processing application, and a synthetic workload with configurable compute and I/O intensity. My evaluation shows that MECBench can be used to answer complex what-if questions pertaining to design and deployment decisions of MEC platforms and applications. My evaluation explores the impact of different combinations of applications, hardware, and network conditions as well as the cost-benefit tradeoff of different designs and configurations.