Cucumber: Renewable-Aware Admission Control for Delay-Tolerant Cloud and Edge Workloads

Wiesner, P., Scheinert, D., Wittkopp, T., Thamsen, L. and Kao, O. (2022) Cucumber: Renewable-Aware Admission Control for Delay-Tolerant Cloud and Edge Workloads. In: 28th International European Conference on Parallel and Distributed Computing (EURO-PAR 2022), Glasgow, Scotland, United Kingdom, 22-26 August 2022, pp. 218-232. ISBN 9783031125966 (doi: 10.1007/978-3-031-12597-3_14)

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Abstract

The growing electricity demand of cloud and edge computing increases operational costs and will soon have a considerable impact on the environment. A possible countermeasure is equipping IT infrastructure directly with on-site renewable energy sources. Yet, particularly smaller data centers may not be able to use all generated power directly at all times, while feeding it into the public grid or energy storage is often not an option. To maximize the usage of renewable excess energy, we propose Cucumber, an admission control policy that accepts delay-tolerant workloads only if they can be computed within their deadlines without the use of grid energy. Using probabilistic forecasting of computational load, energy consumption, and energy production, Cucumber can be configured towards more optimistic or conservative admission. We evaluate our approach on two scenarios using real solar production forecasts for Berlin, Mexico City, and Cape Town in a simulation environment. For scenarios where excess energy was actually available, our results show that Cucumber’s default configuration achieves acceptance rates close to the optimal case and causes 97.0% of accepted workloads to be powered using excess energy, while more conservative admission results in 18.5% reduced acceptance at almost zero grid power usage.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Thamsen, Dr Lauritz
Authors: Wiesner, P., Scheinert, D., Wittkopp, T., Thamsen, L., and Kao, O.
College/School:College of Science and Engineering > School of Computing Science
ISSN:0302-9743
ISBN:9783031125966
Copyright Holders:Copyright © 2022 Springer Nature Switzerland AG
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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