OpenRTiST: End-to-End Benchmarking for Edge Computing
Shilpa George, Thomas Eiszler, Roger Iyengar, Haithem Turki, Ziqiang Feng, Junjue Wang, Padmanabhan Pillai, Mahadev Satyanarayanan
Abstract
The growth of edge computing depends on large-scale deployments of edge infrastructure. Benchmarking applications are needed to compare the performance across different edge deployments and against device-only and cloud-only implementations. In this article, we present OpenRTiST, an open-source application that is simultaneously compute-intensive, bandwidth-hungry, and latency-sensitive. It implements a form of augmented reality that lets you “see the world through the eyes of an artist.” We compare end-to-end application latency over varying network conditions and measure performance across a variety of edge platforms. OpenRTiST is designed to be easily deployed and has been used to showcase the benefits of edge computing.