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Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments

McGough, S; Forshaw, M; Brennan, J; Al Moubayed, N; Bonner, S

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Authors

S McGough

M Forshaw

J Brennan

S Bonner



Abstract

High Throughput Computing (HTC) provides a convenient mechanism for running thousands of tasks. Many HTC systems exploit computers which are provisioned for other purposes by utilising their idle time - volunteer computing. This has great advantages as it gives access to vast quantities of computational power for little or no cost. The downside is that running tasks are sacrificed if the computer is needed for its primary use. Normally terminating the task which must be restarted on a different computer - leading to wasted energy and an increase in task completion time. We demonstrate, through the use of simulation, how we can reduce this wasted energy by targeting tasks at computers less likely to be needed for primary use, predicting this idle time through machine learning. By combining two machine learning approaches, namely Random Forest and MultiLayer Perceptron, we save 51.4% of the energy without significantly affecting the time to complete tasks.

Citation

McGough, S., Forshaw, M., Brennan, J., Al Moubayed, N., & Bonner, S. (2018). Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments. In 2018 Ninth International Green and Sustainable Computing Conference (IGSC) (1-8). https://doi.org/10.1109/igcc.2018.8752115

Conference Name 9th International Green and Sustainable Computing Conference.
Conference Location Pittsburgh, PA, US
Start Date Oct 22, 2018
End Date Oct 24, 2018
Acceptance Date Aug 24, 2018
Online Publication Date Jul 1, 2019
Publication Date 2018
Deposit Date Oct 16, 2018
Publicly Available Date Oct 17, 2018
Publisher Institute of Electrical and Electronics Engineers
Pages 1-8
Book Title 2018 Ninth International Green and Sustainable Computing Conference (IGSC).
DOI https://doi.org/10.1109/igcc.2018.8752115

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Accepted Conference Proceeding (785 Kb)
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