Content Caching-Enhanced Computation Offloading in Mobile Edge Service Networks

Dong, Y; Guo, S; Wang, Q; Yu, S; Yang, Y

Content Caching-Enhanced Computation Offloading in Mobile Edge Service Networks

Dong, Y Guo, S Wang, Q Yu, S

Yang, Y

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Vehicular Technology, 2022, 71, (1), pp. 872-886
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Dong, Y
dc.contributor.author	Guo, S
dc.contributor.author	Wang, Q
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.contributor.author	Yang, Y
dc.date.accessioned	2022-04-10T01:23:57Z
dc.date.available	2022-04-10T01:23:57Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Vehicular Technology, 2022, 71, (1), pp. 872-886
dc.identifier.issn	0018-9545
dc.identifier.issn	1939-9359
dc.identifier.uri	http://hdl.handle.net/10453/156010
dc.description.abstract	Cache enhanced computation offloading as a novel offloading paradigm in mobile edge computing (MEC) can reduce more task execution latency than traditional computation offloading by reusing of computation offloading data. However, existing works only focus on the enhancement between computation offloading and data caching but ignore the competition for cache resources between them. To this end, in this paper, we propose a caching enhanced computation offloading algorithm in mobile edge service networks (MESN), by considering the cache resources competition. We formulate a joint optimization problem of content caching and cache-enhanced computation offloading. Furthermore, we give the optimal caching strategy to achieve the equilibrium between the resources competition. By our offloading algorithm caching strategy, the average response time of computation and content request tasks can get further reduction. In addition, we design two low time complexity algorithms, i.e., mixed caching algorithm and enhanced offloading algorithm, to solve the sub-problems, i.e., smart base station (SBS) caching sub-problem and computation offloading sub-problem, transformed by the original optimization problem. The simulation results show that our algorithms can quickly converge and our scheme can reduce 20.52% average response time of all tasks at most compared with other schemes.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Vehicular Technology
dc.relation.isbasedon	10.1109/TVT.2021.3128772
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	Automobile Design & Engineering
dc.title	Content Caching-Enhanced Computation Offloading in Mobile Edge Service Networks
dc.type	Journal Article
utslib.citation.volume	71
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
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	2022-04-10T01:23:56Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	71
utslib.citation.issue	1

Abstract:

Cache enhanced computation offloading as a novel offloading paradigm in mobile edge computing (MEC) can reduce more task execution latency than traditional computation offloading by reusing of computation offloading data. However, existing works only focus on the enhancement between computation offloading and data caching but ignore the competition for cache resources between them. To this end, in this paper, we propose a caching enhanced computation offloading algorithm in mobile edge service networks (MESN), by considering the cache resources competition. We formulate a joint optimization problem of content caching and cache-enhanced computation offloading. Furthermore, we give the optimal caching strategy to achieve the equilibrium between the resources competition. By our offloading algorithm caching strategy, the average response time of computation and content request tasks can get further reduction. In addition, we design two low time complexity algorithms, i.e., mixed caching algorithm and enhanced offloading algorithm, to solve the sub-problems, i.e., smart base station (SBS) caching sub-problem and computation offloading sub-problem, transformed by the original optimization problem. The simulation results show that our algorithms can quickly converge and our scheme can reduce 20.52% average response time of all tasks at most compared with other schemes.

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http://hdl.handle.net/10453/156010