McGough, A.S. and Al Moubayed, N. and Forshaw, M. (2017) 'Using machine learning in trace-driven energy-aware simulations of high-throughput computing systems.', in Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion (ICPE '17 Companion), April 22 - 26, 2017, L’Aquila, Italy. New York: ACM, pp. 55-60.
When performing a trace-driven simulation of a High Throughput Computing system we are limited to the knowledge which should be available to the system at the current point within the simulation. However, the trace-log contains information we would not be privy to during the simulation. Through the use of Machine Learning we can extract the latent patterns within the trace-log allowing us to accurately predict characteristics of tasks based only on the information we would know. These characteristics will allow us to make better decisions within simulations allowing us to derive better policies for saving energy. We demonstrate that we can accurately predict (up-to 99% accuracy), using oversampling and deep learning, those tasks which will complete while at the same time provide accurate predictions for the task execution time and memory footprint using Random Forest Regression.
|Item Type:||Book chapter|
|Full text:||(AM) Accepted Manuscript|
Download PDF (3257Kb)
|Publisher Web site:||https://doi.org/10.1145/3053600.3053612|
|Publisher statement:||© 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ICPE '17 Companion Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion, https://doi.org/10.1145/10.1145/3053600.3053612|
|Date accepted:||07 March 2017|
|Date deposited:||22 March 2017|
|Date of first online publication:||18 April 2017|
|Date first made open access:||No date available|
Save or Share this output
|Look up in GoogleScholar|