Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82190
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dc.contributorDepartment of Computing-
dc.creatorLi, GS-
dc.creatorLin, QY-
dc.creatorWu, JH-
dc.creatorZhang, Y-
dc.creatorYan, JH-
dc.date.accessioned2020-05-05T05:59:02Z-
dc.date.available2020-05-05T05:59:02Z-
dc.identifier.urihttp://hdl.handle.net/10397/82190-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/en_US
dc.rightsThe following publication G. Li, Q. Lin, J. Wu, Y. Zhang and J. Yan, "Dynamic Computation Offloading Based on Graph Partitioning in Mobile Edge Computing," in IEEE Access, vol. 7, pp. 185131-185139, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2960887en_US
dc.subjectMobile edge computingen_US
dc.subjectOffloading decisionen_US
dc.subjectNode clusteringen_US
dc.subjectOptimal strategyen_US
dc.subjectNash equilibriumen_US
dc.titleDynamic computation offloading based on graph partitioning in mobile edge computingen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage185131-
dc.identifier.epage185139-
dc.identifier.volume7-
dc.identifier.doi10.1109/ACCESS.2019.2960887-
dcterms.abstractMobile edge computing is a new cloud computing paradigm that utilizes small-sized edge clouds to provide real-time services to users. These mobile edge clouds (MECs) are located near users, thereby enabling users to seamlessly access applications that are running on MECs and to easily access MECs. Terminal devices can transfer tasks to MEC servers nearby to improve the quality of computing. In this paper, we study multi-user computation offloading problem for mobile-edge computing in a multichannel wireless interference environment. Then, we analyze the overhead of each mobile devices, and we propose strategies for task scheduling and offloading in a multi-user MEC system. For reducing the energy consumption, we propose a server partitioning algorithm that is based on clustering. We formulate the task offloading decision problem as a multi-user game, which always has a Nash equilibrium. The simulation results demonstrate that our scheme outperforms the traditional offloading strategy in terms of energy consumption.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE access, 19 Dec. 2019, v. 7, p. 185131-185139-
dcterms.isPartOfIEEE access-
dcterms.issued2019-
dc.identifier.isiWOS:000510021700057-
dc.identifier.scopus2-s2.0-85077958051-
dc.identifier.eissn2169-3536-
dc.description.validate202006 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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