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Reduction of wasted energy in a volunteer computing system through Reinforcement Learning

Lookup NU author(s): Dr Matthew Forshaw, Dr Stephen McGough

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Abstract

Volunteer computing systems provide an easy mechanism for users who wish to perform large amounts of High Throughput Computing work. However, if the Volunteer Computing system is deployed over a shared set of computers where interactive users can seize back control of the computers this can lead to wasted computational effort and hence wasted energy. Determining on which resource to deploy a particular piece of work, or even to choose not to deploy the work at the current time, is a difficult problem to solve, depending both on the expected free time available on the computers within the Volunteer computing system and the expected runtime of the work - both of which are difficult to determine a-priori. We develop here a Reinforcement Learning approach to solving this problem and demonstrate that it can provide a reduction in energy consumption between 30% and 53% depending on whether we can tolerate an increase in the overheads incurred.


Publication metadata

Author(s): Forshaw M, McGough AS

Publication type: Report

Publication status: Published

Series Title: School of Computing Science Technical Report Series

Year: 2015

Pages: 43

Print publication date: 01/02/2015

Report Number: 1451

Institution: School of Computing Science, University of Newcastle upon Tyne

Place Published: Newcastle upon Tyne

URL: http://www.cs.ncl.ac.uk/publications/trs/papers/1451.pdf


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