Energy-Aware Streaming Analytics Job Scheduling for Edge Computing

Demetris Trihinas, Moysis Symeonides, Joanna Georgiou, George Pallis, Marios D. Dikaiakos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Energy profiling and optimization are expected to be crucial factors impacting the realisation of the Internet of Things (IoT) as more intelligence is deployed at the network extremes to achieve better response times in the proximity of where data are harvested. To improve the performance of streaming analytics jobs, several schedulers have been designed to tackle key challenges in edge computing realms, including resource heterogeneity and highly volatile network links. However, energy-aware scheduling for streaming analytic jobs is at best, not adequately examined. In this article, we introduce PowerStorm, a scheduler for streaming analytic jobs that is designed to explore trade-offs between performance and energy consumption in geodistributed edge computing settings. We implement our scheduler for Apache Storm and show the scheduler's energy saving capabilities over the Yahoo streaming benchmark with worker nodes featuring heterogeneous power and resource capabilities on both a physical and emulated testbed.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023
PublisherIEEE Computer Society
Pages161-168
Number of pages8
ISBN (Electronic)9798350339826
DOIs
Publication statusPublished - 2023
Event14th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023 - Naples, Italy
Duration: 4 Dec 20236 Dec 2023

Publication series

NameProceedings of the International Conference on Cloud Computing Technology and Science, CloudCom
ISSN (Print)2330-2194
ISSN (Electronic)2330-2186

Conference

Conference14th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023
Country/TerritoryItaly
CityNaples
Period4/12/236/12/23

Keywords

  • Big Data
  • Energy Profiling
  • Internet of Things

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