Canete, A, Djemame, K orcid.org/0000-0001-5811-5263, Amor, M et al. (2 more authors) (2023) A proactive energy-aware auto-scaling solution for edge-based infrastructures. In: Proceedings of the 15th IEEE/ACM International Conference on Utility and Cloud Computing. 15th IEEE/ACM International Conference on Utility and Cloud Computing (UCC2022), 06-09 Dec 2022, Vancouver, Washington, USA. IEEE , pp. 240-247. ISBN 978-1-6654-6087-3
Abstract
Proactive auto-scaling mechanisms in edge-based infrastructures can anticipate user service requests by allocating computing resources while supporting the quality of service needed by a vast range of applications requiring, e.g., a low latency or response time. However, managing the dynamic needs of user service requests is challenging due to the edge infrastructure’s heterogeneity and dynamic nature. Also, minimizing global energy consumption is a must in today’s systems, which should be addressed inherently as part of any resource scaling solution. This paper presents a proactive horizontal auto-scaling framework for edge infrastructures, which takes into account both the base (idle) and dynamic (due to application execution) energy consumption of edge nodes, as well as of the node scaling mechanism. Simulations were performed with the EdgeCloudSim simulator with a workload provided by Shanghai Telecom and the results show up to a 92.5% decrease in energy consumption, a failed request rate of up to 0%, and reasonable execution times of the auto-scaling process for different problem sizes.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EU - European Union Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Dec 2022 13:56 |
Last Modified: | 19 Nov 2024 15:21 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/UCC56403.2022.00044 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193716 |