EdgeBench: A Workflow-based Benchmark for Edge Computing

TL;DR

EdgeBench is a customizable, workflow-based benchmark designed to evaluate the performance and design space of heterogeneous, distributed edge computing systems with data-driven, latency-sensitive workloads.

Contribution

It introduces a flexible benchmarking framework that models diverse edge workloads and system configurations, enabling comprehensive performance analysis.

Findings

Demonstrates the usability of EdgeBench with video analytics and IoT workflows.

Identifies performance bottlenecks in edge systems through workflow and function-level metrics.

Abstract

Edge computing has been developed to utilize multiple tiers of resources for privacy, cost and Quality of Service (QoS) reasons. Edge workloads have the characteristics of data-driven and latency-sensitive. Because of this, edge systems have developed to be both heterogeneous and distributed. The unique characteristics of edge workloads and edge systems have motivated EdgeBench, a workflow-based benchmark aims to provide the ability to explore the full design space of edge workloads and edge systems. EdgeBench is both customizable and representative. It allows users to customize the workflow logic of edge workloads, the data storage backends, and the distribution of the individual workflow stages to different computing tiers. To illustrate the usability of EdgeBench, we also implements two representative edge workflows, a video analytics workflow and an IoT hub workflow that represents two distinct but common edge workloads. Both workflows are evaluated using the workflow-level and function-level metrics reported by EdgeBench to illustrate both the performance bottlenecks of the edge systems and the edge workloads.