Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/139819
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Type: Conference paper
Title: Energy-efficient Edge Server Management for Edge Computing: A Game-theoretical Approach
Author: Cui, G.
He, Q.
Xia, X.
Chen, F.
Yang, Y.
Citation: Proceedings of the International Conference on Parallel Processing, 2023, pp.69-1-69-11
Publisher: Assocation for Computing Machinery
Publisher Place: New York, NY, USA
Issue Date: 2023
Series/Report no.: Proceedings of the International Conference on Parallel Processing
ISBN: 9781450397339
ISSN: 0190-3918
Conference Name: 51st International Conference on Parallel Processing (ICPP) (29 Aug 2022 - 1 Sep 2022 : Bordeaux, France)
Statement of
Responsibility: 
Guangming Cui, Qiang He, Xiaoyu Xia, Feifei Chen, Yun Yang
Abstract: Similar to cloud servers which are well-known energy consumers, edge servers running 24/7 jointly consume a tremendous amount of energy and thus require energy-saving management. However, the unique characteristics of edge computing make it a new and challenging problem to manage edge servers in an energy-efficient manner. First, an individual edge server is usually used to serve a specific region. The temporal distribution of end-users in the area impacts the edge server’s energy utilization. Second, multiple base stations may cover an end-user simultaneously and the end-user can be served by the physical machines attached to any of the base stations. Serving the end-users in an area with minimum physical machines can minimize the edge servers’ overall energy consumption. Third, physical machines facilitating an edge server can be powered off individually when not needed to minimize the edge server’s energy consumption. We formulate this Energy-efficient Edge Server Management (EESM) problem and analyze its problem hardness. Next, a game-theoretical approach, i.e., EESM-G, is proposed to address EESM problems efficiently. The superior performance of EESM-G is tested on a public real-world dataset.
Keywords: Energy-efficient Edge Server Management; edge computing; energy consumption; potential game
Rights: © 2022 Association for Computing Machinery.
DOI: 10.1145/3545008.3545079
Grant ID: http://purl.org/au-research/grants/arc/DP180100212
http://purl.org/au-research/grants/arc/DP200102491
Published version: https://dl.acm.org/doi/proceedings/10.1145/3545008
Appears in Collections:Computer Science publications

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