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OpenRTiST: End-to-End Benchmarking for Edge Computing.

Authors :
George, Shilpa
Eiszler, Thomas
Iyengar, Roger
Turki, Haithem
Feng, Ziqiang
Wang, Junjue
Pillai, Padmanabhan
Satyanarayanan, Mahadev
Source :
IEEE Pervasive Computing; Oct-Dec2020, Vol. 19 Issue 4, p10-18, 9p
Publication Year :
2020

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15361268
Volume :
19
Issue :
4
Database :
Complementary Index
Journal :
IEEE Pervasive Computing
Publication Type :
Academic Journal
Accession number :
147134005
Full Text :
https://doi.org/10.1109/MPRV.2020.3028781