Program Placement Optimization for Storage-constrained Mobile Edge Computing Systems: A Multi-armed Bandit Approach

Feng, M; Krunz, M

Program Placement Optimization for Storage-constrained Mobile Edge Computing Systems: A Multi-armed Bandit Approach

Feng, M Krunz, M

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2021, 00, pp. 149-158
Issue Date:: 2021-06-01

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Field	Value	Language
dc.contributor.author	Feng, M
dc.contributor.author	Krunz, M
dc.date	2021-06-07
dc.date.accessioned	2022-06-08T21:39:54Z
dc.date.available	2022-06-08T21:39:54Z
dc.date.issued	2021-06-01
dc.identifier.citation	2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2021, 00, pp. 149-158
dc.identifier.isbn	9781665422635
dc.identifier.uri	http://hdl.handle.net/10453/158019
dc.description.abstract	Mobile edge computing (MEC) is a promising technology to support computationally intensive mobile applications with stringent delay requirements. As MEC applications become much more diverse and complex, it becomes more challenging for an edge node (EN) with limited storage to keep the program codes of all tasks. In this paper, we investigate the problem of program placement and user association in storage-limited MEC systems. Formulating the problem as a sequential decision-making problem, we first derive the solution for a single EN by transforming the formulation into a multi-armed bandit (MBA) problem and solving it via a Thompson sampling (TS) algorithm. We then propose a solution framework for the multi-EN scenario, where we decompose the original problem into three subproblems and solve them with low-complexity approaches. The first subproblem is to learn the task popularity, which we also formulate as a MAB problem and solve it via a TS algorithm. The second subproblem is optimizing program placement under a given user association and we propose a greedy algorithm to solve it. The last subproblem relates to user association, which is solved by a dual decomposition-based approach. Simulation results show that the average latency achieved by our proposed schemes is 30% to 100% lower than two benchmark schemes and is on average less than 10% higher than a lower bound.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)
dc.relation.ispartof	2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks
dc.relation.isbasedon	10.1109/wowmom51794.2021.00028
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Program Placement Optimization for Storage-constrained Mobile Edge Computing Systems: A Multi-armed Bandit Approach
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Pisa, Italy
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	in_progress	*
dc.date.updated	2022-06-08T21:39:53Z
pubs.finish-date	2021-06-11
pubs.publication-status	Published
pubs.start-date	2021-06-07
pubs.volume	00

Abstract:

Mobile edge computing (MEC) is a promising technology to support computationally intensive mobile applications with stringent delay requirements. As MEC applications become much more diverse and complex, it becomes more challenging for an edge node (EN) with limited storage to keep the program codes of all tasks. In this paper, we investigate the problem of program placement and user association in storage-limited MEC systems. Formulating the problem as a sequential decision-making problem, we first derive the solution for a single EN by transforming the formulation into a multi-armed bandit (MBA) problem and solving it via a Thompson sampling (TS) algorithm. We then propose a solution framework for the multi-EN scenario, where we decompose the original problem into three subproblems and solve them with low-complexity approaches. The first subproblem is to learn the task popularity, which we also formulate as a MAB problem and solve it via a TS algorithm. The second subproblem is optimizing program placement under a given user association and we propose a greedy algorithm to solve it. The last subproblem relates to user association, which is solved by a dual decomposition-based approach. Simulation results show that the average latency achieved by our proposed schemes is 30% to 100% lower than two benchmark schemes and is on average less than 10% higher than a lower bound.

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http://hdl.handle.net/10453/158019