Adaptive Task Offloading Auction for Industrial CPS in Mobile Edge Computing

Luo, S; Wen, Y; Xu, W; Puthal, D

Adaptive Task Offloading Auction for Industrial CPS in Mobile Edge Computing

Luo, S Wen, Y Xu, W Puthal, D

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Publication Type:: Journal Article
Citation:: IEEE Access, 2019, 7 pp. 169055 - 169065
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Luo, S	en_US
dc.contributor.author	Wen, Y	en_US
dc.contributor.author	Xu, W	en_US
dc.contributor.author	Puthal, D https://orcid.org/0000-0002-8332-278X	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	IEEE Access, 2019, 7 pp. 169055 - 169065	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138143
dc.description.abstract	© 2019 IEEE. The emerging intelligent applications in Industrial Cyber-Physical Systems (ICPS), such as product inspection by deep-learning-based image recognition technology, are highly computation-consuming. However, the smart devices without sufficient computing resources fail to handle this kind of applications. Moreover, the Internet has very high latency compared with the local network which fails to meet the requirements of time-sensitive tasks, therefore we can not offload these tasks over the cloud. Mobile Edge Computing (MEC) brings the opportunities to offload the tasks of ICPS to the MEC servers to satisfy strict latency requirements, as well as to meet the demand for security requirements. Considering MEC servers owned by the third parties, resource allocation in MEC should be solved jointly with network economics to maximize the utility of system. In this paper, we investigate the task offloading problem under the access capability, latency and security constraints. Specifically, we present a novel Adaptive Task Offloading (ATO) auction mechanism to determine which MEC server to offload with access capability and security constraints, and how to schedule tasks with various deadline constraints, which incentives the third party of MEC providers to share their computing resources with the maximum profit. According to our theoretical analysis, the proposed auction mechanism has the properties of individual rationality, computational efficiency and truthfulness. Extensive simulations have been conducted to evaluate the performance of ATO auction and the experimental results show our method provides better solutions with the classic greedy algorithms in terms of maximizing the utility of the MEC server.	en_US
dc.relation.ispartof	IEEE Access	en_US
dc.relation.isbasedon	10.1109/ACCESS.2019.2954898	en_US
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Adaptive Task Offloading Auction for Industrial CPS in Mobile Edge Computing	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	en_US
pubs.embargo.period	Not known	en_US
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 Information, Systems and Modelling
utslib.copyright.status	recently_added	*
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

© 2019 IEEE. The emerging intelligent applications in Industrial Cyber-Physical Systems (ICPS), such as product inspection by deep-learning-based image recognition technology, are highly computation-consuming. However, the smart devices without sufficient computing resources fail to handle this kind of applications. Moreover, the Internet has very high latency compared with the local network which fails to meet the requirements of time-sensitive tasks, therefore we can not offload these tasks over the cloud. Mobile Edge Computing (MEC) brings the opportunities to offload the tasks of ICPS to the MEC servers to satisfy strict latency requirements, as well as to meet the demand for security requirements. Considering MEC servers owned by the third parties, resource allocation in MEC should be solved jointly with network economics to maximize the utility of system. In this paper, we investigate the task offloading problem under the access capability, latency and security constraints. Specifically, we present a novel Adaptive Task Offloading (ATO) auction mechanism to determine which MEC server to offload with access capability and security constraints, and how to schedule tasks with various deadline constraints, which incentives the third party of MEC providers to share their computing resources with the maximum profit. According to our theoretical analysis, the proposed auction mechanism has the properties of individual rationality, computational efficiency and truthfulness. Extensive simulations have been conducted to evaluate the performance of ATO auction and the experimental results show our method provides better solutions with the classic greedy algorithms in terms of maximizing the utility of the MEC server.

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http://hdl.handle.net/10453/138143