Multi-agent DRL for joint completion delay and energy consumption with queuing theory in MEC-based IIoT

Wu, G; Xu, Z; Zhang, H; Shen, S; Yu, S

Multi-agent DRL for joint completion delay and energy consumption with queuing theory in MEC-based IIoT

Wu, G Xu, Z Zhang, H Shen, S Yu, S

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Publisher:: ACADEMIC PRESS INC ELSEVIER SCIENCE
Publication Type:: Journal Article
Citation:: Journal of Parallel and Distributed Computing, 2023, 176, pp. 80-94
Issue Date:: 2023-06-01

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Field	Value	Language
dc.contributor.author	Wu, G
dc.contributor.author	Xu, Z
dc.contributor.author	Zhang, H
dc.contributor.author	Shen, S
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2024-03-12T04:07:12Z
dc.date.available	2024-03-12T04:07:12Z
dc.date.issued	2023-06-01
dc.identifier.citation	Journal of Parallel and Distributed Computing, 2023, 176, pp. 80-94
dc.identifier.issn	0743-7315
dc.identifier.issn	1096-0848
dc.identifier.uri	http://hdl.handle.net/10453/176559
dc.description.abstract	In the Industrial Internet of Things (IIoT), there exist numerous sensor devices with weak computing power and small energy storage. To meet the real-time and big data computing requirements of industrial production, EIIoT (Edge computing-based IIoT) that combines mobile edge computing with IIoT has emerged. It is necessary to offload computing tasks to nearby edge servers for data storage and processing in EIIoT, thus inevitably causing the edge servers to overload. To this end, we propose a jointly constrained optimization model of delay and energy consumption based on queuing theory; this model can effectively solve the task offloading problem in EIIoT. Subsequently, to satisfy the unique offloading requirements of EIIoT, we improve the MAPPO (multi agent proximal policy optimization) algorithm structure to form a lightweight optimal task offloading algorithm called Multi-Agent Deep Reinforcement Learning based on Queuing theory (MAQDRL), which is more suitable for EIIoT. In the algorithm, we systematically integrate queuing theory and use Multi-Agent Deep Reinforcement Learning (MADRL) to obtain the optimal offloading strategy in dynamic and random multiuser offloading environments. We also improve the structure of neural networks of MADRL by analyzing the structural characteristics of the input data. As a result, the algorithm that we proposed exhibits good convergence and exceptional performance in terms of the task arrival rate, bandwidth, energy consumption, latency and other indicators. The simulation results indicate that compared with other classical algorithms, MAQDRL is effective for solving the EIIoT offloading problem.
dc.language	English
dc.publisher	ACADEMIC PRESS INC ELSEVIER SCIENCE
dc.relation.ispartof	Journal of Parallel and Distributed Computing
dc.relation.isbasedon	10.1016/j.jpdc.2023.02.008
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing
dc.subject.classification	Distributed Computing
dc.subject.classification	4606 Distributed computing and systems software
dc.title	Multi-agent DRL for joint completion delay and energy consumption with queuing theory in MEC-based IIoT
dc.type	Journal Article
utslib.citation.volume	176
utslib.for	0803 Computer Software
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 Computer Science
pubs.organisational-group	University of Technology Sydney/Strength - CCSP - Centre for Cyber Security and Privacy
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-12T04:07:11Z
pubs.publication-status	Published
pubs.volume	176

Abstract:

In the Industrial Internet of Things (IIoT), there exist numerous sensor devices with weak computing power and small energy storage. To meet the real-time and big data computing requirements of industrial production, EIIoT (Edge computing-based IIoT) that combines mobile edge computing with IIoT has emerged. It is necessary to offload computing tasks to nearby edge servers for data storage and processing in EIIoT, thus inevitably causing the edge servers to overload. To this end, we propose a jointly constrained optimization model of delay and energy consumption based on queuing theory; this model can effectively solve the task offloading problem in EIIoT. Subsequently, to satisfy the unique offloading requirements of EIIoT, we improve the MAPPO (multi agent proximal policy optimization) algorithm structure to form a lightweight optimal task offloading algorithm called Multi-Agent Deep Reinforcement Learning based on Queuing theory (MAQDRL), which is more suitable for EIIoT. In the algorithm, we systematically integrate queuing theory and use Multi-Agent Deep Reinforcement Learning (MADRL) to obtain the optimal offloading strategy in dynamic and random multiuser offloading environments. We also improve the structure of neural networks of MADRL by analyzing the structural characteristics of the input data. As a result, the algorithm that we proposed exhibits good convergence and exceptional performance in terms of the task arrival rate, bandwidth, energy consumption, latency and other indicators. The simulation results indicate that compared with other classical algorithms, MAQDRL is effective for solving the EIIoT offloading problem.

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http://hdl.handle.net/10453/176559