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

McGough, A.S.; Forshaw, M.

doi:10.1016/j.suscom.2014.08.014

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

McGough, A.S.; Forshaw, M.

Authors

A.S. McGough

M. Forshaw

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.

Citation

McGough, A., & Forshaw, M. (2014). Reduction of wasted energy in a volunteer computing system through Reinforcement Learning. Sustainable Computing: Informatics and Systems, 4(4), 262-275. https://doi.org/10.1016/j.suscom.2014.08.014

Journal Article Type	Article
Publication Date	Dec 1, 2014
Deposit Date	Dec 24, 2014
Publicly Available Date	Jan 14, 2015
Journal	Sustainable Computing
Print ISSN	2210-5379
Publisher	Elsevier
Peer Reviewed	Peer Reviewed
Volume	4
Issue	4
Pages	262-275
DOI	https://doi.org/10.1016/j.suscom.2014.08.014
Keywords	Volunteer computing, Energy, Reinforcement Learning, Task scheduling.

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Copyright Statement
NOTICE: this is the author’s version of a work that was accepted for publication in Sustainable Computing: Informatics and Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Sustainable Computing: Informatics and Systems, 4, December 2014, 10.1016/j.suscom.2014.08.014.