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Article: Stochastic Control of Computation Offloading to a Helper With a Dynamically Loaded CPU

TitleStochastic Control of Computation Offloading to a Helper With a Dynamically Loaded CPU
Authors
KeywordsTask analysis
Stochastic processes
Energy consumption
Wireless communication
Optimization
Issue Date2019
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, 2019, v. 18 n. 2, p. 1247-1262 How to Cite?
AbstractDue to densification of wireless networks, there exist abundance of idling computation resources at (network) edge helpers (e.g., base stations and handheld computers). These resources can be scavenged by offloading heavy computation tasks from small Internet-of-Things (IoT) devices (e.g., sensors and wearable computing devices) in proximity, thereby overcoming their limitations and lengthening their battery lives. However, unlike dedicated servers, the spare resources offered by edge helpers are random and intermittent. Thus, it is essential to intelligently control a user (IoT device) the amounts of data for offloading and local computing so as to ensure that a computation task can be finished in time-consuming minimum energy. In this paper, we design energy-efficient control policies in a computation offloading system with a random channel and a helper with a dynamically loaded CPU (due to the primary service). Specifically, the policy adopted by the helper aims at determining the sizes of offloaded and locally computed data for a given task in different slots such that the total energy consumption for transmission and local CPU is minimized under a task-deadline constraint. As the result, the polices endow an offloading user robustness against channel-and-helper randomness besides balancing offloading and local computing. By modeling the channel and helper CPU as Markov chains, the problem of offloading control is converted into a Markov decision process. Though dynamic programming (DP) for numerically solving the problem does not yield the optimal policies in closed form, we leverage the procedure to quantify the optimal policy structure and apply the result to design optimal or sub-optimal policies. For three cases ranging from zero, small to large helper buffers, the low complexity of the policies overcomes the “curse of dimensionality” in DP arising from joint consideration of channel, helper CPU, and buffer states.
Persistent Identifierhttp://hdl.handle.net/10722/277223
ISSN
2017 Impact Factor: 5.888
2015 SCImago Journal Rankings: 2.340

 

DC FieldValueLanguage
dc.contributor.authorTao, Y-
dc.contributor.authorYOU, C-
dc.contributor.authorZhang, P-
dc.contributor.authorHuang, K-
dc.date.accessioned2019-09-20T08:46:57Z-
dc.date.available2019-09-20T08:46:57Z-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Wireless Communications, 2019, v. 18 n. 2, p. 1247-1262-
dc.identifier.issn1536-1276-
dc.identifier.urihttp://hdl.handle.net/10722/277223-
dc.description.abstractDue to densification of wireless networks, there exist abundance of idling computation resources at (network) edge helpers (e.g., base stations and handheld computers). These resources can be scavenged by offloading heavy computation tasks from small Internet-of-Things (IoT) devices (e.g., sensors and wearable computing devices) in proximity, thereby overcoming their limitations and lengthening their battery lives. However, unlike dedicated servers, the spare resources offered by edge helpers are random and intermittent. Thus, it is essential to intelligently control a user (IoT device) the amounts of data for offloading and local computing so as to ensure that a computation task can be finished in time-consuming minimum energy. In this paper, we design energy-efficient control policies in a computation offloading system with a random channel and a helper with a dynamically loaded CPU (due to the primary service). Specifically, the policy adopted by the helper aims at determining the sizes of offloaded and locally computed data for a given task in different slots such that the total energy consumption for transmission and local CPU is minimized under a task-deadline constraint. As the result, the polices endow an offloading user robustness against channel-and-helper randomness besides balancing offloading and local computing. By modeling the channel and helper CPU as Markov chains, the problem of offloading control is converted into a Markov decision process. Though dynamic programming (DP) for numerically solving the problem does not yield the optimal policies in closed form, we leverage the procedure to quantify the optimal policy structure and apply the result to design optimal or sub-optimal policies. For three cases ranging from zero, small to large helper buffers, the low complexity of the policies overcomes the “curse of dimensionality” in DP arising from joint consideration of channel, helper CPU, and buffer states.-
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.subjectTask analysis-
dc.subjectStochastic processes-
dc.subjectEnergy consumption-
dc.subjectWireless communication-
dc.subjectOptimization-
dc.titleStochastic Control of Computation Offloading to a Helper With a Dynamically Loaded CPU-
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.2018.2890653-
dc.identifier.scopuseid_2-s2.0-85061695457-
dc.identifier.hkuros305398-
dc.identifier.volume18-
dc.identifier.issue2-
dc.identifier.spage1247-
dc.identifier.epage1262-
dc.publisher.placeUnited States-

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