Energy Efficient Computation Offloading in Mobile Edge Computing

167 Pages·2022·3.063 MB·English

by  Ying Chen, Ning Zhang, Yuan Wu, Sherman Shen

Wireless Networks
Ying Chen
Ning Zhang
Yuan Wu
Sherman Shen
Energy Efficient
Computation
Offloading in
Mobile Edge
Computing
Wireless Networks
SeriesEditor
XueminShermanShen,UniversityofWaterloo,Waterloo,ON,Canada
The purpose of Springer’s Wireless Networks book series is to establish the state
of the art and set the course for future research and development in wireless
communication networks. The scope of this series includes not only all aspects
of wireless networks (including cellular networks, WiFi, sensor networks, and
vehicular networks), but related areas such as cloud computing and big data.
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volumes,textbooksandhandbooks.
**Indexing:WirelessNetworksisindexedinEBSCOdatabasesandDPLB**
Ying Chen (cid:129) Ning Zhang (cid:129) Yuan Wu (cid:129)
Sherman Shen
Energy Efficient
Computation Offloading in
Mobile Edge Computing
YingChen NingZhang
ComputerSchool DepartmentofElectricalandComputer
BeijingInformationScienceand Engineering
TechnologyUniversity UniversityofWindsor
Beijing,China Windsor,ON,Canada
YuanWu ShermanShen
StateKeyLabofInternetofThingsfor DepartmentofElectricalandComputer
SmartCity Engineering
UniversityofMacau UniversityofWaterloo
Taipa,China Waterloo,ON,Canada
ISSN2366-1186 ISSN2366-1445 (electronic)
WirelessNetworks
ISBN978-3-031-16821-5 ISBN978-3-031-16822-2 (eBook)
https://doi.org/10.1007/978-3-031-16822-2
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Preface
With the proliferation of mobile devices and development of Internet of Things
(IoT), more and more computation-intensive and delay-sensitive applications are
running on terminal devices, which result in high-energy consumption and heavy
computationloadofdevices.Duetothesizeandhardwareconstraints,thebattery
lifetime and computing capacity of terminal devices are limited. Consequently,
it is hard to process all of the tasks locally while satisfying Quality of Service
(QoS) requirements for devices. Mobile Cloud Computing (MCC) is a potential
technologytosolvetheproblem,whereterminaldevicescanalleviatetheoperating
load by offloading tasks to the cloud with abundant computing resources for
processing.However,ascloudserversaregenerallylocatedfarawayfromterminal
devices, data transmission from terminal devices to cloud servers would incur
a large amount of energy consumption and transmission delay. Mobile Edge
Computing(MEC)isconsideredasapromisingparadigmthatdeployscomputing
resources at the network edge in proximity of terminal devices. With the help
of MEC, terminal devices can achieve better computing performance and battery
lifetime while ensuring QoS. The introduction of MEC also brings the challenges
of computation offloading and resources management under energy-constrained
and dynamic channel conditions. It is of importance to design energy-efficient
computation offloading strategies while considering the dynamics of task arrival
andsystemenvironments.
In this book, we provide a comprehensive review and in-depth discussion of
the state-of-the-art research literature and propose energy-efficient computation
offloading and resources management for MEC, covering task offloading, channel
allocation, frequency scaling, and resource scheduling. In Chap. 1, we provide
a comprehensive review on the background of MCC, MEC, and computation
offloading.Then,wepresentthecharacteristicsandtypicalapplicationsofMCCand
MEC.Finally,wesummarizethechallengesofcomputationoffloadinginMEC.In
Chap.2,weproposeanEnergyEfficientDynamicComputingOffloading(EEDCO)
scheme to minimize energy consumption and guarantee terminal devices’ delay
performance. In Chap. 3, to further improve energy efficiency combined with tail
energy, we propose a Computation Offloading and Frequency Scaling for Energy
v
vi Preface
Efficiency (COFSEE) scheme to jointly deal with the stochastic task allocation
and CPU-cycle frequency scaling to achieve the minimum energy consumption
while guaranteeing the system stability. In Chap. 4, we investigate delay-aware
and energy-efficient computation offloading in a dynamic MEC system with
multiple edge servers, and an end-to-end Deep Reinforcement Learning (DRL)
approach is presented to select the best edge server for offloading and allocate
the optimal computational resource such that the expected long-term utility is
maximized. In Chap. 5, we study the multi-task computation offloading in multi-
access MEC via non-orthogonal multiple access (NOMA), and accounting for the
time-varying channel conditions between the ST and edge-computing servers, an
onlinealgorithm,whichisbasedonDRL,isproposedtoefficientlylearnthenear-
optimaloffloadingsolutions.InChap.6,weconcludethebookandgivedirections
forfutureresearch.
We believe that the presented computation offloading and energy management
solutions and the corresponding research results in this book can provide some
valuable insights for practical applications of MEC and motivate new ideas for
futureMEC-enabledIoTnetworks.
We would like to thank Mr. Kaixin Li and Mr. Yongchao Zhang from Beijing
Information Science and Technology for their contributions to this book. We also
wouldliketothankallmembersoftheBBCRgroupattheUniversityofWaterloo
fortheirvaluablediscussionsandinsightfulsuggestions.SpecialthanksgotoSusan
Lagerstrom-Fife and Shina Harshavardhan from Springer Nature for their help
throughoutthepublicationprocess.
Beijing,China YingChen
Windsor,ON,Canada NingZhang
Macao,China YuanWu
Waterloo,ON,Canada ShermanShen
Acknowledgements
This work was supported in part by the National Natural Science Foundation of
China under Grants 61902029 and 62072490, the Scientific Research Project of
BeijingMunicipalEducationCommissionunderGrantKM202011232015, inpart
by the Joint Scientific Research Project Funding Scheme between Macao Science
and Technology Development Fund and the Ministry of Science and Technology
of the People’s Republic of China under Grant 0066/2019/AMJ, in part by the
Macao Science and Technology Development Fund under Grants 0060/2019/A1
and 0162/2019/A3, in part by FDCT SKL-IOTSC(UM)-2021-2023, in part by the
Guangdong Basic and Applied Basic Research Foundation (2022A1515011287),
andinpartbytheNaturalSciencesandEngineeringResearchCouncilofCanada.
vii
Contents
1 Introduction .................................................................. 1
1.1 Background.............................................................. 1
1.1.1 MobileCloudComputing ...................................... 2
1.1.2 MobileEdgeComputing ....................................... 8
1.1.3 ComputationOffloading........................................ 12
1.2 Challenges............................................................... 17
1.3 Contributions............................................................ 19
1.4 BookOutline............................................................ 21
References..................................................................... 21
2 DynamicComputationOffloadingforEnergyEfficiencyin
MobileEdgeComputing .................................................... 27
2.1 SystemModelandProblemStatement ................................ 27
2.1.1 NetworkModel ................................................. 28
2.1.2 TaskOffloadingModel......................................... 28
2.1.3 TaskQueuingModel ........................................... 30
2.1.4 EnergyConsumptionModel ................................... 30
2.1.5 ProblemStatement.............................................. 31
2.2 EEDCO:EnergyEfficientDynamicComputingOffloading
forMobileEdgeComputing............................................ 32
2.2.1 JointOptimizationofEnergyandQueue...................... 32
2.2.2 DynamicComputationOffloadingforMobileEdge
Computing ...................................................... 34
2.2.3 Trade-OffBetweenQueueBacklogandEnergyEfficiency .. 36
2.2.4 ConvergenceandComplexityAnalysis........................ 37
2.3 PerformanceEvaluation ................................................ 40
2.3.1 ImpactsofParameters.......................................... 40
2.3.2 PerformanceComparisonwithEAandQWSchemes........ 48
2.4 LiteratureReview ....................................................... 50
2.5 Summary ................................................................ 57
References..................................................................... 57
ix
x Contents
3 Energy Efficient Offloading and Frequency Scaling for
InternetofThingsDevices .................................................. 61
3.1 SystemModelandProblemFormulation.............................. 61
3.1.1 NetworkModel ................................................. 63
3.1.2 TaskModel...................................................... 63
3.1.3 QueuingModel ................................................. 65
3.1.4 EnergyConsumptionModel ................................... 65
3.1.5 ProblemFormulation........................................... 67
3.2 COFSEE:ComputationOffloadingandFrequencyScaling
forEnergyEfficiencyofInternetofThingsDevices.................. 68
3.2.1 ProblemTransformation........................................ 68
3.2.2 OptimalFrequencyScaling .................................... 71
3.2.3 LocalComputationAllocation................................. 72
3.2.4 MECComputationAllocation ................................. 74
3.2.5 TheoreticalAnalysis............................................ 75
3.3 PerformanceEvaluation ................................................ 79
3.3.1 ImpactsofSystemParameters ................................. 80
3.3.2 PerformanceComparisonwithRLE,RMEandTS
Schemes......................................................... 84
3.4 LiteratureReview ....................................................... 87
3.5 Summary ................................................................ 91
References..................................................................... 92
4 Deep Reinforcement Learning for Delay-Aware and
Energy-EfficientComputationOffloading................................ 97
4.1 SystemModelandProblemFormulation.............................. 97
4.1.1 SystemModel................................................... 97
4.1.2 ProblemFormulation........................................... 99
4.2 ProposedDRLMethod ................................................. 103
4.2.1 DataPrepossessing ............................................. 104
4.2.2 DRLModel ..................................................... 105
4.2.3 Training ......................................................... 108
4.3 PerformanceEvaluation ................................................ 111
4.4 LiteratureReview ....................................................... 117
4.5 Summary ................................................................ 120
References..................................................................... 120
5 Energy-Efficient Multi-Task Multi-Access Computation
OffloadingviaNOMA....................................................... 123
5.1 SystemModelandProblemFormulation.............................. 123
5.1.1 Motivation....................................................... 123
5.1.2 SystemModel................................................... 125
5.1.3 ProblemFormulation........................................... 126
5.2 LEEMMO: Layered Energy-Efficient Multi-Task
Multi-AccessAlgorithm................................................ 127
5.2.1 LayeredDecompositionofJointOptimizationProblem...... 128