Task Partitioning and User Association for Latency Minimization in Mobile Edge Computing Networks

Feng, M; Krunz, M; Zhang, W

Task Partitioning and User Association for Latency Minimization in Mobile Edge Computing Networks

Feng, M Krunz, M Zhang, W

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2021, 00, pp. 1-6
Issue Date:: 2021-07-19

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Field	Value	Language
dc.contributor.author	Feng, M
dc.contributor.author	Krunz, M
dc.contributor.author	Zhang, W
dc.date	2021-05-10
dc.date.accessioned	2022-06-08T21:52:53Z
dc.date.available	2022-06-08T21:52:53Z
dc.date.issued	2021-07-19
dc.identifier.citation	IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2021, 00, pp. 1-6
dc.identifier.isbn	9781665404433
dc.identifier.uri	http://hdl.handle.net/10453/158022
dc.description.abstract	Mobile edge computing (MEC) is a promising solution to support emerging delay-sensitive mobile applications. With MEC servers deployed at the network edge, the computational tasks generated by these applications can be offloaded to edge nodes (ENs) and quickly executed there. Meanwhile, with the projected large number of IoT devices, the communication and computational resources allocated to each user can be quite limited, providing low-latency MEC services becomes challenging. In this paper, we investigate the problem of task partitioning and user association in an MEC system, aiming to minimize the average latency of all users. We assume that each task can be partitioned into multiple independent subtasks that can be executed on local devices (e.g., vehicles), MEC servers, and/or cloud servers; each user can be associated with one of the nearby ENs. We formulate a mixed-integer programming problem to determine the task partitioning ratios and user association. Such a problem is solved by decomposing it into two subproblems. The lower-level subproblem relates to task partitioning under a given user association, which can be solved optimally. The higher-level subproblem is user association, where we propose a dual decomposition-based approach to solve it. Simulation results show that, compared to benchmark schemes, the proposed schemes reduce the average latency by approximately 50%.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
dc.relation.ispartof	IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops
dc.relation.isbasedon	10.1109/infocomwkshps51825.2021.9484605
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Task Partitioning and User Association for Latency Minimization in Mobile Edge Computing Networks
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Vancouver, BC, Canada
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	2022-06-08T21:52:52Z
pubs.finish-date	2021-05-13
pubs.publication-status	Published
pubs.start-date	2021-05-10
pubs.volume	00

Abstract:

Mobile edge computing (MEC) is a promising solution to support emerging delay-sensitive mobile applications. With MEC servers deployed at the network edge, the computational tasks generated by these applications can be offloaded to edge nodes (ENs) and quickly executed there. Meanwhile, with the projected large number of IoT devices, the communication and computational resources allocated to each user can be quite limited, providing low-latency MEC services becomes challenging. In this paper, we investigate the problem of task partitioning and user association in an MEC system, aiming to minimize the average latency of all users. We assume that each task can be partitioned into multiple independent subtasks that can be executed on local devices (e.g., vehicles), MEC servers, and/or cloud servers; each user can be associated with one of the nearby ENs. We formulate a mixed-integer programming problem to determine the task partitioning ratios and user association. Such a problem is solved by decomposing it into two subproblems. The lower-level subproblem relates to task partitioning under a given user association, which can be solved optimally. The higher-level subproblem is user association, where we propose a dual decomposition-based approach to solve it. Simulation results show that, compared to benchmark schemes, the proposed schemes reduce the average latency by approximately 50%.

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http://hdl.handle.net/10453/158022