Automated Selection of Energy-efficient Operating System Configurations

Herzog B, Hügel F, Reif S, Hönig T, Schröder-Preikschat W (2021)


Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2021

Publisher: Association for Computing Machinery

City/Town: New York, NY, USA

Pages Range: 309-315

Conference Proceedings Title: e-Energy '21: Proceedings of the Twelfth ACM International Conference on Future Energy Systems

Event location: Virtual Event, Italy

ISBN: 9781450383332

DOI: 10.1145/3447555.3465327

Abstract

Edge computing systems need to use their available resources ef- ficiently. Operating systems and run-time systems offer numer- ous configuration parameters to fine-tune their behaviour, which are adjustable to balance the execution time and energy demand of applications. However, the number of parameters produces a vast space of possible configurations and the exact consequences on non-functional properties are often poorly documented. Thus, identifying efficient configurations proves challenging.

This paper presents PolaR, an approach for the automated de- termination of energy-efficient configurations, as well as an im- plementation for Linux. PolaR combines application profiles and system-level information to select efficient configurations dynami- cally and does not require application changes. Configurations are predicted by an oracle either based on linear models or neural net- works. Our evaluation shows that PolaR improves the mean en- ergy efficiency by 11.5 % for typical applications.

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How to cite

APA:

Herzog, B., Hügel, F., Reif, S., Hönig, T., & Schröder-Preikschat, W. (2021). Automated Selection of Energy-efficient Operating System Configurations. In e-Energy '21: Proceedings of the Twelfth ACM International Conference on Future Energy Systems (pp. 309-315). Virtual Event, Italy: New York, NY, USA: Association for Computing Machinery.

MLA:

Herzog, Benedict, et al. "Automated Selection of Energy-efficient Operating System Configurations." Proceedings of the 2nd International Workshop on Energy-Efficient Learning at the Edge, Virtual Event, Italy New York, NY, USA: Association for Computing Machinery, 2021. 309-315.

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