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Article: Asynchronous Mobile-Edge Computation Offloading: Energy-Efficient Resource Management

TitleAsynchronous Mobile-Edge Computation Offloading: Energy-Efficient Resource Management
Authors
KeywordsProcessor scheduling
Resource management
Optimal scheduling
Wireless communication
Computational modeling
Issue Date2018
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7693
Citation
IEEE Transactions on Wireless Communications, 2018, v. 17 n. 11, p. 7590-7605 How to Cite?
AbstractMobile-edge computation offloading (MECO) is an emerging technology for enhancing mobiles' computation capabilities and prolonging their battery lifetime by offloading intensive computation from mobiles to nearby servers, such as base stations. In this paper, we study the energy-efficient resource-management policy for the asynchronous MECO system, where the mobiles have heterogeneous input-data arrival time instants and computation deadlines. First, we consider the general case with arbitrary arrival-deadline orders. Based on the monomial energy-consumption model for data transmission, an optimization problem is formulated to minimize the total mobile-energy consumption under the time-sharing and computation-deadline constraints. The optimal resource-management policy for data partitioning (for offloading and local computing) and time division (for transmissions) is obtained in (semi-)closed-form expression by using the block coordinate decent method. To gain further insight, we study the optimal resource-management design for two special cases. First, consider the case of identical arrival-deadline orders, i.e., a mobile with input data arriving earlier also needs to complete computation earlier. The optimization problem is reduced to two sequential problems corresponding to the optimal scheduling order and joint data-partitioning and time-division given the optimal order. It is found that the optimal time-division policy tends to equalize the defined effective computing power among offloading mobiles via time sharing. Furthermore, this solution approach is extended to the case of reverse arrival-deadline orders. The corresponding time-division policy is derived by a proposed transformation-and-scheduling approach that first determines the total offloading duration and data size for each mobile in the transformation phase and then specifies the offloading intervals for each mobile in the scheduling phase.
Persistent Identifierhttp://hdl.handle.net/10722/277220
ISSN
2017 Impact Factor: 5.888
2015 SCImago Journal Rankings: 2.340

 

DC FieldValueLanguage
dc.contributor.authorYOU, C-
dc.contributor.authorZeng, Y-
dc.contributor.authorZhang, R-
dc.contributor.authorHuang, K-
dc.date.accessioned2019-09-20T08:46:55Z-
dc.date.available2019-09-20T08:46:55Z-
dc.date.issued2018-
dc.identifier.citationIEEE Transactions on Wireless Communications, 2018, v. 17 n. 11, p. 7590-7605-
dc.identifier.issn1536-1276-
dc.identifier.urihttp://hdl.handle.net/10722/277220-
dc.description.abstractMobile-edge computation offloading (MECO) is an emerging technology for enhancing mobiles' computation capabilities and prolonging their battery lifetime by offloading intensive computation from mobiles to nearby servers, such as base stations. In this paper, we study the energy-efficient resource-management policy for the asynchronous MECO system, where the mobiles have heterogeneous input-data arrival time instants and computation deadlines. First, we consider the general case with arbitrary arrival-deadline orders. Based on the monomial energy-consumption model for data transmission, an optimization problem is formulated to minimize the total mobile-energy consumption under the time-sharing and computation-deadline constraints. The optimal resource-management policy for data partitioning (for offloading and local computing) and time division (for transmissions) is obtained in (semi-)closed-form expression by using the block coordinate decent method. To gain further insight, we study the optimal resource-management design for two special cases. First, consider the case of identical arrival-deadline orders, i.e., a mobile with input data arriving earlier also needs to complete computation earlier. The optimization problem is reduced to two sequential problems corresponding to the optimal scheduling order and joint data-partitioning and time-division given the optimal order. It is found that the optimal time-division policy tends to equalize the defined effective computing power among offloading mobiles via time sharing. Furthermore, this solution approach is extended to the case of reverse arrival-deadline orders. The corresponding time-division policy is derived by a proposed transformation-and-scheduling approach that first determines the total offloading duration and data size for each mobile in the transformation phase and then specifies the offloading intervals for each mobile in the scheduling phase.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7693-
dc.relation.ispartofIEEE Transactions on Wireless Communications-
dc.rightsIEEE Transactions on Wireless Communications. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectProcessor scheduling-
dc.subjectResource management-
dc.subjectOptimal scheduling-
dc.subjectWireless communication-
dc.subjectComputational modeling-
dc.titleAsynchronous Mobile-Edge Computation Offloading: Energy-Efficient Resource Management-
dc.typeArticle-
dc.identifier.emailHuang, K: huangkb@eee.hku.hk-
dc.identifier.authorityHuang, K=rp01875-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TWC.2018.2868710-
dc.identifier.scopuseid_2-s2.0-85053614419-
dc.identifier.hkuros305394-
dc.identifier.volume17-
dc.identifier.issue11-
dc.identifier.spage7590-
dc.identifier.epage7605-
dc.publisher.placeUnited States-

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