Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/117741
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Type: Conference paper
Title: In-vivo and offline optimisation of energy use in the presence of small energy signals: case study on a popular Android library
Author: Bokhari, M.A.
Alexander, B.
Wagner, M.
Citation: MobiQuitous '18 Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 2018 / pp.207-215
Publisher: Association for Computing Machinery
Issue Date: 2018
Series/Report no.: ACM International Conference Proceeding Series
ISBN: 9781450360937
Conference Name: EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (Mobiquitous) (05 Nov 2018 - 07 Nov 2018 : New York City, NY)
Statement of
Responsibility: 
Mahmoud A. Bokhari, Brad Alexander, Markus Wagner
Abstract: Energy demands of applications on mobile platforms are increasing. As a result, there has been a growing interest in optimising their energy efficiency. As mobile platforms are fast-changing, diverse and complex, the optimisation of energy use is a non-trivial task. To date, most energy optimisation methods either use models or external meters to estimate energy use. Unfortunately, it becomes hard to build widely applicable energy models, and external meters are neither cheap nor easy to set up. To address this issue, we run application variants in-vivo on the phone and use a precise internal battery monitor to measure energy use. We describe a methodology for optimising a target application in-vivo and with application-specific models derived from the device's own internal meter based on jiffies and lines of code. We demonstrate that this process produces a significant improvement in energy efficiency with limited loss of accuracy.
Keywords: Non-functional properties; energy consumption; mobile applications; Android; multi-objective optimisation
Rights: © 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM.
RMID: 0030107601
DOI: 10.1145/3286978.3287014
Grant ID: http://purl.org/au-research/grants/arc/DE160100850
Published version: https://doi.org/10.1145/3286978
Appears in Collections:Computer Science publications

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