OpenRTiST: End-to-End Benchmarking for Edge Computing

Shilpa George, Thomas Eiszler, Roger Iyengar, Haithem Turki, Ziqiang Feng, Junjue Wang, Padmanabhan Pillai, Mahadev Satyanarayanan
2020 IEEE pervasive computing  
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
more » ... f 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. 2020 Published by the IEEE Computer Society This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
doi:10.1109/mprv.2020.3028781 fatcat:w63esar2gvdwzf3dc3cftcra2q