Online Optimization of Energy-Efficient User Association and Workload Offloading for Mobile Edge Computing

Zhang, J; Cui, Q; Zhang, X; Ni, W; Lyu, X; Pan, M; Tao, X

Online Optimization of Energy-Efficient User Association and Workload Offloading for Mobile Edge Computing

Zhang, J Cui, Q Zhang, X Ni, W

Lyu, X Pan, M Tao, X

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Vehicular Technology, 2022, 71, (2), pp. 1974-1988
Issue Date:: 2022-02-01

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Field	Value	Language
dc.contributor.author	Zhang, J
dc.contributor.author	Cui, Q
dc.contributor.author	Zhang, X
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Lyu, X
dc.contributor.author	Pan, M
dc.contributor.author	Tao, X
dc.date.accessioned	2023-04-06T04:55:07Z
dc.date.available	2023-04-06T04:55:07Z
dc.date.issued	2022-02-01
dc.identifier.citation	IEEE Transactions on Vehicular Technology, 2022, 71, (2), pp. 1974-1988
dc.identifier.issn	0018-9545
dc.identifier.issn	1939-9359
dc.identifier.uri	http://hdl.handle.net/10453/169331
dc.description.abstract	This paper presents a new stochastic optimization framework for user association and task offloading in mobile edge computing (MEC) networks with spatial and temporal variations of computing power, channel quality and connection capacity between different MEC servers. The new framework minimizes a quadratic penalty function which balances the energy consumption and fairness of the devices. Lyapunov optimization is first applied to eliminate the time coupling of the framework, leading to a mixed-integer program (MIP) of user association and offloading scheduling at every time slot. While solving the scheduling using linear programming, we convert the user association to a minimum cost maximum flow problem by interpreting edge servers and devices as two disjoint vertexes. We solve the minimum-cost maximum flow problem efficiently by using the Ford-Fulkerson algorithm. Corroborated by simulations, the proposed approach is asymptotically optimal and outperforms alternative approaches in terms of energy saving and fairness.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Vehicular Technology
dc.relation.isbasedon	10.1109/TVT.2021.3133940
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	Automobile Design & Engineering
dc.title	Online Optimization of Energy-Efficient User Association and Workload Offloading for Mobile Edge Computing
dc.type	Journal Article
utslib.citation.volume	71
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
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	2023-04-06T04:55:06Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	71
utslib.citation.issue	2

Abstract:

This paper presents a new stochastic optimization framework for user association and task offloading in mobile edge computing (MEC) networks with spatial and temporal variations of computing power, channel quality and connection capacity between different MEC servers. The new framework minimizes a quadratic penalty function which balances the energy consumption and fairness of the devices. Lyapunov optimization is first applied to eliminate the time coupling of the framework, leading to a mixed-integer program (MIP) of user association and offloading scheduling at every time slot. While solving the scheduling using linear programming, we convert the user association to a minimum cost maximum flow problem by interpreting edge servers and devices as two disjoint vertexes. We solve the minimum-cost maximum flow problem efficiently by using the Ford-Fulkerson algorithm. Corroborated by simulations, the proposed approach is asymptotically optimal and outperforms alternative approaches in terms of energy saving and fairness.

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http://hdl.handle.net/10453/169331