GreenEdge: New Perspectives to Energy Management and Supply in Mobile Edge Computing

119 Pages·2022·6.275 MB·English

by  Guoming Tang, Deke Guo, Kui Wu

SPRINGER BRIEFS IN COMPUTER SCIENCE
Guoming Tang
Deke Guo
Kui Wu
GreenEdge: New
Perspectives
to Energy
Management and
Supply in Mobile
Edge Computing
12 3
SpringerBriefs in Computer Science
SeriesEditors
StanZdonik,BrownUniversity,Providence,RI,USA
ShashiShekhar,UniversityofMinnesota,Minneapolis,MN,USA
XindongWu,UniversityofVermont,Burlington,VT,USA
LakhmiC.Jain,UniversityofSouthAustralia,Adelaide,SA,Australia
DavidPadua,UniversityofIllinoisUrbana-Champaign,Urbana,IL,USA
XueminShermanShen,UniversityofWaterloo,Waterloo,ON,Canada
BorkoFurht,FloridaAtlanticUniversity,BocaRaton,FL,USA
V.S.Subrahmanian,UniversityofMaryland,CollegePark,MD,USA
MartialHebert,CarnegieMellonUniversity,Pittsburgh,PA,USA
KatsushiIkeuchi,UniversityofTokyo,Tokyo,Japan
BrunoSiciliano,UniversitàdiNapoliFedericoII,Napoli,Italy
SushilJajodia,GeorgeMasonUniversity,Fairfax,VA,USA
NewtonLee,InstituteforEducation,ResearchandScholarships,LosAngeles,CA,
USA
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Guoming Tang (cid:129) Deke Guo (cid:129) Kui Wu
GreenEdge: New
Perspectives to Energy
Management and Supply
in Mobile Edge Computing
The First Book on Green Edge Computing
GuomingTang DekeGuo
DepartmentofBroadbandCommunication CollegeofSystemsEngineering
PengChengLaboratory NationalUniversityofDefenseTechnology
Shenzhen,China Changsha,Hunan,China
KuiWu
DepartmentofComputerScience
UniversityofVictoria
Victoria,BC,Canada
ISSN2191-5768 ISSN2191-5776 (electronic)
SpringerBriefsinComputerScience
ISBN978-981-16-9689-3 ISBN978-981-16-9690-9 (eBook)
https://doi.org/10.1007/978-981-16-9690-9
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Preface
The5Gtechnologyhasbeencommercializedworldwideandisexpectedtoprovide
superiorperformancewithenhancedmobilebroadband,ultra-lowlatencytransmis-
sion,andmassiveIoTconnections.Meanwhile,theedgecomputingparadigmgets
populartoprovidedistributedcomputingandstorageresourcesinproximitytothe
users(atthenetworkedge).Comparedwithcloudcomputing,edgecomputinghas
theadvantageofconductinglatency-criticaltasksbyhavingthemexecutedcloserto
endusers.Asedgeservicesandapplicationsprosper,5Gandedgecomputingwillbe
tightlycoupledandcontinuouslypromoteeachotherforward.Embracingthistrend,
however,mobileusers,infrastructureproviders,andserviceprovidersareallfaced
with the energydilemma.From the user side, battery-poweredmobile devicesare
muchconstrainedbybatterylife,whereasmobileplatformsandappsnowadaysare
usuallypower-hungry.Fromtheinfrastructureandserviceproviderside,theenergy
costofedgefacilities,particularly5Gbasestationsandedgedatacenters,accounts
foralargeproportionofoperatingexpensesandhasbecomeahugeburden.
Inthisbook,weintroduceourrecentworktacklingtheenergyissuesinmobile
edgecomputing.WenametheconstellationofworkGreenEdge.Unliketraditional
approaches, solutions, and frameworks, we deal with energy management and
supplyproblemsfromtotallynewperspectives.Formobileusers,(i)weinvestigate
their low-battery anxiety through a large-scale user survey and quantify their
anxiety degree and video watching behavior concerning the battery status; and
(ii) by leveraging the quantified low-battery anxiety model, we further develop a
low-power video streaming solution at the network edge to save mobile devices’
energyandalleviateusers’low-batteryanxiety.Foredgeinfrastructureandservice
operators, (i) we devise an optimal backup power deployment framework to cut
down the backup battery cost in 5G networks; (ii) we investigate the cost-saving
potentialoftransformingthebackupbatteriestoadistributedbatteryenergystorage
v
vi Preface
system;and(iii)wedesignanintegratedrenewableenergysupplyarchitectureand
asoftware-definedpowersupplymechanismtopursuenet-zeroedgedatacentersin
thefutureedgecomputingenvironment.
Shenzhen,China GuomingTang
Changsha,China DekeGuo
Victoria,BC,Canada KuiWu
Contents
1 Introduction .................................................................. 1
1.1 When5GMeetsEdgeComputing ..................................... 1
1.2 TheEnergyDilemma................................................... 2
1.3 KeyProblemsandContributions....................................... 3
1.4 ContentOrganization................................................... 4
2 InvestigatingLow-BatteryAnxietyofMobileUsers..................... 7
2.1 Introduction ............................................................. 7
2.2 RelatedWork............................................................ 9
2.3 ASurveyOver2000+MobileUsers................................... 10
2.4 QuantificationofLow-BatteryAnxiety................................ 11
2.4.1 ExtractionofLBACurve....................................... 11
2.4.2 ObservationsandAnalysis..................................... 12
2.4.3 LessonsLearntfromLBAQuantification ..................... 15
2.5 ImpactsofLBAonVideoWatching ................................... 15
2.5.1 ExtractionofVideoAbandoningLikelihoodCurve .......... 15
2.5.2 ObservationsandAnalysis..................................... 16
2.5.3 AdviceforVideoStreamingServices ......................... 19
2.6 Ethics.................................................................... 19
2.7 Conclusion .............................................................. 19
3 UserEnergyandLBAAwareMobileVideoStreaming ................. 21
3.1 Introduction ............................................................. 21
3.2 BackgroundandRelatedWork......................................... 24
3.2.1 BackgroundofLow-BatteryAnxiety.......................... 24
3.2.2 BackgroundofDisplayPowerSaving......................... 24
3.2.3 WorkRelatedtoThisWork .................................... 25
3.3 LBASurveyandModelling ............................................ 26
3.3.1 DataCollection ................................................. 26
3.3.2 LBACurveExtraction.......................................... 27
3.3.3 InsightsonLBAAlleviation ................................... 28
vii
viii Contents
3.4 LPVS:Low-PowerVideoStreaming................................... 28
3.4.1 ScenarioOverview.............................................. 28
3.4.2 ModelsforPowerConsumptioninVideoStreaming......... 30
3.4.3 ModelsforEnergyStatusandLow-BatteryAnxiety ......... 31
3.4.4 VideoStreamingCapacityattheEdge ........................ 32
3.4.5 JointOptimizationforEnergySavingandAnxiety
Reduction ....................................................... 32
3.5 SolutionMethodology.................................................. 33
3.5.1 TheDifficulties ................................................. 33
3.5.2 InformationCompacting ....................................... 33
3.5.3 ATwo-PhaseHeuristicforJointOptimization................ 35
3.5.4 Determineγ withBayesianInference........................ 36
n
3.6 LBAModelUpdating................................................... 38
3.6.1 AnalysisofLBAHeterogeneity................................ 38
3.6.2 LocalLBAModelUpdating ................................... 38
3.7 Implementations......................................................... 40
3.7.1 Real-WorldVideoStreamingTraces........................... 40
3.7.2 LPVSEmulationandSetups................................... 41
3.8 PerformanceEvaluations ............................................... 43
3.8.1 LPVSwithSufficientEdgeResource.......................... 43
3.8.2 LPVSwithLimitedEdgeResource............................ 44
3.8.3 ImpactofLPVSonLow-BatteryUsers ....................... 45
3.8.4 LPVSwithUpdatedLBAModels ............................. 46
3.8.5 OverheadofLPVSandImpactonOtherQoEMetrics....... 47
3.9 Conclusion .............................................................. 48
4 OptimalBackupPowerAllocationfor5GBaseStations................ 51
4.1 Introduction ............................................................. 51
4.1.1 SpatialDimension .............................................. 52
4.1.2 TemporalDimension ........................................... 53
4.2 RelatedWork............................................................ 53
4.3 BSPowerMeasurementsandObservations ........................... 54
4.3.1 PowerConsumptionof4Gand5GBSs ....................... 54
4.3.2 PowerConsumptionof5GBSMajorComponents........... 55
4.3.3 MultiplexingGainwithBackupPowerSharing............... 55
4.4 SystemModel........................................................... 57
4.4.1 ScenarioOverview.............................................. 58
4.4.2 TrafficLoadandPowerDemand............................... 59
4.5 OptimalBackupPowerAllocation..................................... 59
4.5.1 AnalysisofPowerOutagesandNetworkFailure............. 59
4.5.2 ConditionofNetworkReliability.............................. 61
4.5.3 BackupPowerDeploymentConstraints....................... 62
4.5.4 BackupPowerAllocationOptimization....................... 62
Contents ix
4.6 ExperimentalEvaluations .............................................. 63
4.6.1 ExperimentSetup............................................... 63
4.6.2 ResultsandAnalysis............................................ 63
4.7 Conclusion .............................................................. 65
5 ReusingBackupBatteriesforPowerDemandReshapingin5G ....... 67
5.1 Introduction ............................................................. 67
5.2 SystemModels.......................................................... 69
5.2.1 ScenarioOverview.............................................. 69
5.2.2 BSPowerSupplyandDemand ................................ 70
5.2.3 BatterySpecification............................................ 71
5.3 PowerDemandReshapingviaBESSScheduling..................... 71
5.3.1 EnergyCostwithBESS........................................ 72
5.3.2 BatteryDegradationCost....................................... 73
5.3.3 OptimalBESSOperationScheduling ......................... 74
5.3.4 ProblemAnalysis............................................... 75
5.4 ADRL-BasedApproachtoDistributedBESSScheduling........... 77
5.4.1 DRL Based BESS Scheduling:Components
andConcepts.................................................... 77
5.4.2 RewardFunctionDesign ....................................... 78
5.4.3 LearningProcessDesign....................................... 78
5.5 ExperimentalEvaluations .............................................. 81
5.5.1 ExperimentSetup............................................... 81
5.5.2 GeneralPerformanceatCostReductionwithBESS.......... 83
5.5.3 CaseStudiesofDRL-BasedBESSScheduling............... 85
5.5.4 ROIsofDifferentBESSDeployments......................... 86
5.6 RelatedWork............................................................ 87
5.6.1 GeneralSystemPeakPowerShavingwithBESS............. 87
5.6.2 DCPeakPowerShavingwithCentralizedBESS............. 88
5.6.3 DCPeakPowerShavingwithDistributedBESS ............. 88
5.7 Conclusion .............................................................. 89
6 Software-DefinedPowerSupplytoGeo-DistributedEdgeDCs ........ 91
6.1 Introduction ............................................................. 91
6.2 ArchitectureofSoftware-DefinedPowerSupply(SDPS)............. 92
6.2.1 MotivationandDesignRationales............................. 93
6.2.2 ArchitectureDesign ............................................ 93
6.3 Two-PhaseOptimizationinSoftware-DefinedPowerSupply........ 95
6.3.1 SystemModel................................................... 95
6.3.2 Phase-I:ConstructingGreenCells............................. 96
6.3.3 Phase-II:BESSDischarging/ChargingOperations ........... 97
6.4 ExperimentalEvaluations .............................................. 99
6.4.1 ExperimentsSetup.............................................. 99
6.4.2 PerformanceComparison ...................................... 99
6.5 Conclusion .............................................................. 101