McGough, S. and Forshaw, M. and Brennan, J. and Al Moubayed, N. and 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). , pp. 1-8.
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.
|Item Type:||Book chapter|
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://doi.org/10.1109/IGCC.2018.8752115|
|Publisher statement:||© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Date accepted:||24 August 2018|
|Date deposited:||17 October 2018|
|Date of first online publication:||01 July 2019|
|Date first made open access:||No date available|
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