OpenRTiST: End-to-End Benchmarking for Edge Computing
| Autor: | Shilpa George, Thomas Eiszler, Haithem Turki, Mahadev Satyanarayanan, Junjue Wang, Ziqiang Feng, Roger Iyengar, Padmanabhan Pillai |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
020203 distributed computing
Ubiquitous computing business.industry Computer science Distributed computing Cloud computing 02 engineering and technology Benchmarking Computer Science Applications Computational Theory and Mathematics End-to-end principle Server 0202 electrical engineering electronic engineering information engineering Augmented reality business Implementation Software Edge computing |
| Zdroj: | IEEE Pervasive Computing. 19:10-18 |
| ISSN: | 1558-2590 1536-1268 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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