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

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

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


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. (C) 2014 Elsevier Inc. All rights reserved.


Publication metadata

Author(s): McGough AS, Forshaw M

Publication type: Article

Publication status: Published

Journal: Sustainable Computing: Informatics and Systems

Year: 2014

Volume: 4

Issue: 4

Pages: 262-275

Print publication date: 01/12/2014

Online publication date: 10/09/2014

Acceptance date: 11/08/2014

Date deposited: 29/09/2018

ISSN (print): 2210-5379

ISSN (electronic): 2210-5387

Publisher: Elsevier BV

URL: http://dx.doi.org/10.1016/j.suscom.2014.08.014

DOI: 10.1016/j.suscom.2014.08.014


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