Towards Automated System-Level Energy-Efficiency Optimisation using Machine Learning

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

Publication Language: English

Publication Type: Conference contribution, Abstract of a poster

Publication year: 2021

Publisher: Association for Computing Machinery, Inc

City/Town: New York, NY, USA

Pages Range: 274-275

Conference Proceedings Title: e-Energy 2021 - Proceedings of the 2021 12th ACM International Conference on Future Energy Systems

Event location: Online IT

ISBN: 9781450383332

DOI: 10.1145/3447555.3466566

Abstract

Modern computing systems need to execute applications in an energy-efficient manner. To this end, operating systems, middleware, and run-time systems offer plenty of parameters that support fine-tuning their behaviour. However, their individual and combined impact on performance and power draw is so complex that this optimisation potential is often ignored in practice. This paper therefore discusses a cross-layer system design that uses machine learning internally to enable fine-tuning run-time systems to their current workload. Our approach includes all layers, from the hardware to the application, considering both performance and power draw.

Authors with CRIS profile

Benedict Herzog Lehrstuhl für Informatik 4 (Verteilte Systeme und Betriebssysteme) Stefan Reif Lehrstuhl für Informatik 4 (Verteilte Systeme und Betriebssysteme) Wolfgang Schröder-Preikschat Lehrstuhl für Informatik 4 (Verteilte Systeme und Betriebssysteme)

Involved external institutions

Ruhr-Universität Bochum (RUB) DE Germany (DE)

How to cite

APA:

Herzog, B., Reif, S., Hügel, F., Hönig, T., & Schröder-Preikschat, W. (2021, June). Towards Automated System-Level Energy-Efficiency Optimisation using Machine Learning. Poster presentation at 12th ACM International Conference on Future Energy Systems, e-Energy 2021, Online, IT.

MLA:

Herzog, Benedict, et al. "Towards Automated System-Level Energy-Efficiency Optimisation using Machine Learning." Presented at 12th ACM International Conference on Future Energy Systems, e-Energy 2021, Online Ed. Association for Computing Machinery, New York, NY, USA: Association for Computing Machinery, Inc, 2021.

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