Dynamic resource allocation through workload prediction for energy efficient computing

Adeel Ahmed, David Brown, Alexander Gegov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Rapid and continuous increase in online information exchange and data based services has led to an increase in enterprise data centres. Energy efficient computing is key to a cost effective operation for all such enterprise IT systems. In this paper we propose dynamic resource allocation in server based IT systems through workload prediction for energy efficient computing. We use CPU core as a dynamic resource that can be allocated and deallocated based on predicted workload. We use online workload prediction as opposed to offline statistical analysis of workload characteristics. We use online learning and workload prediction using neural network for online dynamic resource allocation for energy efficient computing. We also analyse the effect of dynamic resource allocation on clients by measuring the request response time to clients for variable number of cores in operation. We show that dynamic resource allocation through workload prediction in server based IT systems can provide a cost effective, energy efficient and reliable operation without effecting quality of experience for clients.
Original languageEnglish
Title of host publicationAdvances in Computational Intelligence Systems
Subtitle of host publicationContributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK
EditorsP. Angelov, A. Gegov, C. Jayne, Q. Shen
PublisherSpringer
Pages35-44
Number of pages10
ISBN (Electronic)978-3-319-46562-3
ISBN (Print)978-3-319-46561-6
DOIs
Publication statusPublished - 1 Jan 2017
Event16th Annual UK Workshop on Computational Intelligence - Lancaster University, Lancaster, United Kingdom
Duration: 7 Sep 20169 Sep 2016
http://wp.lancs.ac.uk/ukci2016/
http://wp.lancs.ac.uk/ukci2016/

Publication series

NameAdvances in Intelligent Systems and Computing
Volume513
ISSN (Print)2194-5357

Conference

Conference16th Annual UK Workshop on Computational Intelligence
Abbreviated titleUKCI 2016
Country/TerritoryUnited Kingdom
CityLancaster
Period7/09/169/09/16
Internet address

Keywords

  • energy efficient computing
  • dynamic resource
  • allocation
  • workload prediction
  • neural network

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