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A Hybrid Distributed EA Approach for Energy Optimisation on Smartphones

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Abstract

For many people, mobile platforms are now an essential part of everyday life. A defining feature of mobile platforms is their reliance on battery performance. Due to this reliance, there is a pressing need for mobile applications that minimise their own impact on batteries. While mobile platforms are improving their capabilities in terms of policing the energy use of applications and rationing energy-hungry devices, mobile application developers still lack knowledge in how to write energy efficient programs. Recent work in automatic program improvement using heuristic search over randomly generated program variants has shown some promise in terms of producing reductions in programs’ energy-use. A challenge in this work is accurately measuring the energy-use of program variants. One approach to measurement is to use each platform’s internal meter to assess variants on the device itself. This approach has advantages in terms of measuring actual energy-use on each platform but is not ideal for the search for program variants that perform well across multiple platforms. The work in this paper addresses this problem by using an island-like evolutionary search mode to simultaneously evolve variants on multiple platforms. Island models of evolutionary search conduct search on multiple platforms in parallel and share promising variants. The results show that this approach has advantages over isolated evolution in terms of speeding up evolution on each platform and improving the efficiency of search. Validation results show that the island-inspired model is able to evolve variants with good cross-platform performance. In addition, it evolves a solution that outperforms best found solutions using a sequential evolutionary algorithm on it is native platform with an effect size greater than 90%.

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Notes

  1. Rebound Spring Animations for Android: https://github.com/facebookarchive/rebound-js, Accessed April 2021.

  2. Our results can be retrieved from https://github.com/mahmoud-bokhari/distributed_GI

  3. The optimisation framework that runs on the PC can be found at https://github.com/mahmoud-bokhari/SmartDevicesEnergyOptimiser

  4. The smart devices and logger can be found at https://github.cs.adelaide.edu.au/a1600329/BatteryMonitor

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Authors and Affiliations

  1. Software Engineering Research Group, Computer Science Department, Taibah University, Medina, Kingdom of Saudi Arabia

    Mahmoud A. Bokhari

  2. Optimisation and Logistics, School of Computer Science, University of Adelaide, Adelaide, Australia

    Bradley Alexander

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  1. Mahmoud A. Bokhari
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  2. Bradley Alexander
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Correspondence to Mahmoud A. Bokhari.

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Communicated by: Aldeida Aleti, Annibale Panichella and Shin Yoo

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This article belongs to the Topical Collection: Advances in Search-Based Software Engineering (SSBSE)

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Bokhari, M.A., Alexander, B. A Hybrid Distributed EA Approach for Energy Optimisation on Smartphones. Empir Software Eng 27, 155 (2022). https://doi.org/10.1007/s10664-022-10188-5

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