Modeling on Energy-Efficiency Computation Offloading Using Probabilistic Action Generating

Wang, C; Lu, W; Peng, S; Qu, Y; Wang, G; Yu, S

Modeling on Energy-Efficiency Computation Offloading Using Probabilistic Action Generating

Wang, C Lu, W Peng, S Qu, Y Wang, G Yu, S

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2022, 9, (20), pp. 20681-20692
Issue Date:: 2022-10-15

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Field	Value	Language
dc.contributor.author	Wang, C
dc.contributor.author	Lu, W
dc.contributor.author	Peng, S
dc.contributor.author	Qu, Y
dc.contributor.author	Wang, G
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2023-04-11T01:57:46Z
dc.date.available	2023-04-11T01:57:46Z
dc.date.issued	2022-10-15
dc.identifier.citation	IEEE Internet of Things Journal, 2022, 9, (20), pp. 20681-20692
dc.identifier.issn	2327-4662
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/169495
dc.description.abstract	Wireless-powered mobile-edge computing (MEC) emerges as a crucial component in the Internet of Things (IoTs). It can cope with the fundamental performance limitations of low-power networks, such as wireless sensor networks or mobile networks. Although computation offloading and resource allocation in MEC have been studied with different optimization objectives, performance optimization in larger-scale systems still needs to be further improved. More importantly, energy efficiency is also a key issue as well as computation offloading and resource allocation for wireless-powered MEC. In this article, we investigate the joint optimization of computation rate and energy consumption under limited resources, and propose an online offloading model to search for the asymptotically optimal offloading and resource allocation strategy. First, the joint optimization problem is modeled as a mixed integer programming (MIP) problem. Second, a deep reinforcement learning (DRL)-based method, energy efficiency computation offloading using probabilistic action generating (ECOPG), is designed to generate the joint optimization policy for computation offloading and resource allocation. Finally, to avoid the curse of dimensionality in large network scales, an action exploration mechanism based on probability is introduced to accelerate the convergence rate by targeted sampling and dynamic experience replay. The experimental results demonstrate that the proposed methods significantly outperform other DRL-based methods in energy consumption, and gain better computation rate and execution efficiency at the same time. With the expansion of the network scale, the improvements become more apparent.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2022.3175760
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.title	Modeling on Energy-Efficiency Computation Offloading Using Probabilistic Action Generating
dc.type	Journal Article
utslib.citation.volume	9
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
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 Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-11T01:57:45Z
pubs.issue	20
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	20

Abstract:

Wireless-powered mobile-edge computing (MEC) emerges as a crucial component in the Internet of Things (IoTs). It can cope with the fundamental performance limitations of low-power networks, such as wireless sensor networks or mobile networks. Although computation offloading and resource allocation in MEC have been studied with different optimization objectives, performance optimization in larger-scale systems still needs to be further improved. More importantly, energy efficiency is also a key issue as well as computation offloading and resource allocation for wireless-powered MEC. In this article, we investigate the joint optimization of computation rate and energy consumption under limited resources, and propose an online offloading model to search for the asymptotically optimal offloading and resource allocation strategy. First, the joint optimization problem is modeled as a mixed integer programming (MIP) problem. Second, a deep reinforcement learning (DRL)-based method, energy efficiency computation offloading using probabilistic action generating (ECOPG), is designed to generate the joint optimization policy for computation offloading and resource allocation. Finally, to avoid the curse of dimensionality in large network scales, an action exploration mechanism based on probability is introduced to accelerate the convergence rate by targeted sampling and dynamic experience replay. The experimental results demonstrate that the proposed methods significantly outperform other DRL-based methods in energy consumption, and gain better computation rate and execution efficiency at the same time. With the expansion of the network scale, the improvements become more apparent.

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http://hdl.handle.net/10453/169495