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

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Bibliographic Details
Published in:IEEE pervasive computing 2020-10, Vol.19 (4), p.10-18
Main Authors: George, Shilpa, Eiszler, Thomas, Iyengar, Roger, Turki, Haithem, Feng, Ziqiang, Wang, Junjue, Pillai, Padmanabhan, Satyanarayanan, Mahadev
Format: Article
Language:English
Subjects:
Augmented reality
Benchmarks
Cloud computing
Edge computing
Feeds
Graphics processing units
Image edge detection
Network latency
Performance evaluation
Servers
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Summary: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.
ISSN:1536-1268
1558-2590
DOI:10.1109/MPRV.2020.3028781