Joint Resource Allocation and Load Management for Cooling-Aware Mobile-Edge Computing

Chen, X; Lu, Z; Ni, W; Wang, X; Zhang, S; Xu, S

Joint Resource Allocation and Load Management for Cooling-Aware Mobile-Edge Computing

Chen, X Lu, Z Ni, W

Wang, X Zhang, S Xu, S

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 2020, 2020-June, pp. 1-6
Issue Date:: 2020-07-27

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Field	Value	Language
dc.contributor.author	Chen, X
dc.contributor.author	Lu, Z
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Wang, X
dc.contributor.author	Zhang, S
dc.contributor.author	Xu, S
dc.date	2020-06-07
dc.date.accessioned	2021-04-14T22:35:24Z
dc.date.available	2021-04-14T22:35:24Z
dc.date.issued	2020-07-27
dc.identifier.citation	ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 2020, 2020-June, pp. 1-6
dc.identifier.isbn	9781728150895
dc.identifier.issn	1550-3607
dc.identifier.uri	http://hdl.handle.net/10453/148114
dc.description.abstract	In this paper, we jointly design resource allocation and load management in a mobile-edge computing (MEC) system with wireless power transfer (WPT), to minimize the total energy consumption of the BS, while meeting computation latency requirements. For the first time, the cooling energy, which is non-negligible, is considered to minimize the energy consumption of the MEC system. By orchestrating the alternative optimization technique, Lagrange duality method and subgradient method, we decompose the original optimization problem and obtain the optimal solution in a semi-closed form. Extensive numerical tests corroborate the merits of the proposed algorithm over existing benchmarks in terms of energy saving.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	ICC 2020 - 2020 IEEE International Conference on Communications (ICC)
dc.relation.ispartof	ICC 2020 - 2020 IEEE International Conference on Communications
dc.relation.isbasedon	10.1109/icc40277.2020.9148795
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Joint Resource Allocation and Load Management for Cooling-Aware Mobile-Edge Computing
dc.type	Conference Proceeding
utslib.citation.volume	2020-June
utslib.location.activity	Dublin, Ireland
utslib.for	0805 Distributed Computing
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2021-04-14T22:35:23Z
pubs.finish-date	2020-06-11
pubs.publication-status	Published
pubs.start-date	2020-06-07
pubs.volume	2020-June

Abstract:

In this paper, we jointly design resource allocation and load management in a mobile-edge computing (MEC) system with wireless power transfer (WPT), to minimize the total energy consumption of the BS, while meeting computation latency requirements. For the first time, the cooling energy, which is non-negligible, is considered to minimize the energy consumption of the MEC system. By orchestrating the alternative optimization technique, Lagrange duality method and subgradient method, we decompose the original optimization problem and obtain the optimal solution in a semi-closed form. Extensive numerical tests corroborate the merits of the proposed algorithm over existing benchmarks in terms of energy saving.

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http://hdl.handle.net/10453/148114