Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/126985
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Type: Conference paper
Title: Towards rigorous validation of energy optimisation experiments
Author: Bokhari, M.A.
Alexander, B.
Wagner, M.
Citation: Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO'20), 2020 / vol.abs/2004.04500, pp.1232-1240
Publisher: Association for Computing Machinery
Publisher Place: New York
Issue Date: 2020
ISBN: 9781450371285
Conference Name: Genetic and Evolutionary Computation Conference (GECCO) (08 Jul 2020 - 12 Jul 2020 : Cancún, Mexico)
Statement of
Responsibility: 
Mahmoud A. Bokhari, Brad Alexander, Markus Wagner
Abstract: The optimisation of software energy consumption is of growing importance across all scales of modern computing, i.e., from embedded systems to data-centres. Practitioners in the field of Search-Based Software Engineering and Genetic Improvement of Software acknowledge that optimising software energy consumption is difficult due to noisy and expensive fitness evaluations. However, it is apparent from results to date that more progress needs to be made in rigorously validating optimisation results. This problem is pressing because modern computing platforms have highly complex and variable behaviour with respect to energy consumption. To compare solutions fairly we propose in this paper a new validation approach called R3-validation which exercises software variants in a rotated-round-robin order. Using a case study, we present an in-depth analysis of the impacts of changing system states on software energy usage, and we show how R3-validation mitigates these. We compare it with current validation approaches across multiple devices and operating systems, and we show that it aligns best with actual platform behaviour.
Keywords: Non-functional properties; energy consumption; mobile applications; Android
Rights: © 2020 Association for Computing Machinery.
RMID: 1000019084
DOI: 10.1145/3377930.3390245
Grant ID: http://purl.org/au-research/grants/arc/DE160100850
Published version: https://dl.acm.org/doi/proceedings/10.1145/3377930
Appears in Collections:Computer Science publications

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