Computation Offloading Method Using Stochastic Games for Software-Defined-Network-Based Multiagent Mobile Edge Computing

Wu, G; Wang, H; Zhang, H; Zhao, Y; Yu, S; Shen, S

Computation Offloading Method Using Stochastic Games for Software-Defined-Network-Based Multiagent Mobile Edge Computing

Wu, G Wang, H Zhang, H Zhao, Y Yu, S

Shen, S

Permalink

Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2023, 10, (20), pp. 17620-17634
Issue Date:: 2023-10-15

Closed Access

	Filename	Description	Size
	Computation_Offloading_Method_Using_Stochastic_Games_for_Software-Defined-Network-Based_Multiagent_Mobile_Edge_Computing.pdf	Published version	3.73 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Wu, G
dc.contributor.author	Wang, H
dc.contributor.author	Zhang, H
dc.contributor.author	Zhao, Y
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.contributor.author	Shen, S
dc.date.accessioned	2024-03-12T00:33:19Z
dc.date.available	2024-03-12T00:33:19Z
dc.date.issued	2023-10-15
dc.identifier.citation	IEEE Internet of Things Journal, 2023, 10, (20), pp. 17620-17634
dc.identifier.issn	2327-4662
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/176517
dc.description.abstract	In the scenario of Industry 4.0, mobile smart devices (SDs) on production lines have to process massive amounts of data. These computing tasks sometimes far exceed the computing capability of SDs and require lots of energy and time to process. How to effectively reduce energy consumption and latency is necessary to be solved. To this end, we first propose a software-defined network (SDN)-based mobile edge computing (MEC) system. In the MEC system, SDs can offload computation tasks to edge servers to decrease the processing latency and avoid the waste of energy. At the same time, taking advantage of SDN's programmability, scalability, and isolation of the control plane and the data plane, an SDN controller can manage edge devices within the MEC system. Second, based on a stochastic game, we study the computation offloading and resource allocation problems in the MEC system and establish a stochastic game-based computation offloading model. Furthermore, we prove that the multiuser stochastic game in this system can achieve Nash Equilibrium. We further consider each SD as an independent agent and design a stochastic game-based resource allocation algorithm with prioritized experience replays (SGRA-PERs) to minimize energy consumption and processing latency with Multiagent Reinforcement Learning. Experiment results demonstrate that the proposed SGRA-PER is superior to MADDPG, Q-Mix, and MAPPO algorithms, which can significantly reduce the processing delay and energy consumption with dynamic resource allocation. Moreover, SGRA-PER can still keep a higher performance under the increase of SDs, which can be applied in a large-scale MEC system.
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.2023.3277541
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Computation Offloading Method Using Stochastic Games for Software-Defined-Network-Based Multiagent Mobile Edge Computing
dc.type	Journal Article
utslib.citation.volume	10
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
pubs.organisational-group	University of Technology Sydney/Strength - CCSP - Centre for Cyber Security and Privacy
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-12T00:33:18Z
pubs.issue	20
pubs.publication-status	Published
pubs.volume	10
utslib.citation.issue	20

Abstract:

In the scenario of Industry 4.0, mobile smart devices (SDs) on production lines have to process massive amounts of data. These computing tasks sometimes far exceed the computing capability of SDs and require lots of energy and time to process. How to effectively reduce energy consumption and latency is necessary to be solved. To this end, we first propose a software-defined network (SDN)-based mobile edge computing (MEC) system. In the MEC system, SDs can offload computation tasks to edge servers to decrease the processing latency and avoid the waste of energy. At the same time, taking advantage of SDN's programmability, scalability, and isolation of the control plane and the data plane, an SDN controller can manage edge devices within the MEC system. Second, based on a stochastic game, we study the computation offloading and resource allocation problems in the MEC system and establish a stochastic game-based computation offloading model. Furthermore, we prove that the multiuser stochastic game in this system can achieve Nash Equilibrium. We further consider each SD as an independent agent and design a stochastic game-based resource allocation algorithm with prioritized experience replays (SGRA-PERs) to minimize energy consumption and processing latency with Multiagent Reinforcement Learning. Experiment results demonstrate that the proposed SGRA-PER is superior to MADDPG, Q-Mix, and MAPPO algorithms, which can significantly reduce the processing delay and energy consumption with dynamic resource allocation. Moreover, SGRA-PER can still keep a higher performance under the increase of SDs, which can be applied in a large-scale MEC system.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/176517