Table Of Content(cid:2)
FogandFogonomics
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Fog and Fogonomics
ChallengesandPracticesofFogComputing,
Communication,Networking,Strategy,andEconomics
Editedby
YangYang
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ShanghaiInstituteofFogComputingTechnology(SHIFT)
ShanghaiTechUniversity
Shanghai,China
JianweiHuang
TheChineseUniversityofHongKong
Shenzhen,China
TaoZhang
NationalInstituteofStandardsandTechnology(NIST)
Gaithersburg,MD,USA
JoeWeinman
XFORMALLC
Flanders,NJ,USA
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Thiseditionfirstpublished2020
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Toourfamilies.
–Yang,Jianwei,Tao,andJoe
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vii
Contents
ListofContributors xvii
Preface xxi
1 FogComputingandFogonomics 1
YangYang,JianweiHuang,TaoZhang,andJoeWeinman
2 CollaborativeMechanismforHybridFog-CloudScenarios 7
XaviMasip,EvaMarín,JordiGarcia,andSergiSànchez
(cid:2) 2.1 TheCollaborativeScenario 7 (cid:2)
2.1.1 TheF2CModel 11
2.1.1.1 TheLayeringArchitecture 13
2.1.1.2 TheFogNode 14
2.1.1.3 F2CasaService 16
2.1.2 TheF2CControlArchitecture 19
2.1.2.1 HierarchicalArchitecture 20
2.1.2.2 MainFunctionalBlocks 24
2.1.2.3 ManagingControlData 25
2.1.2.4 SharingResources 26
2.2 BenefitsandApplicability 28
2.3 TheChallenges 29
2.3.1 ResearchChallenges 30
2.3.1.1 WhataResourceis 30
2.3.1.2 Categorization 30
2.3.1.3 Identification 31
2.3.1.4 Clustering 33
2.3.1.5 ResourcesDiscovery 33
2.3.1.6 ResourceAllocation 34
2.3.1.7 Reliability 35
2.3.1.8 QoS 36
2.3.1.9 Security 36
2.3.2 IndustryChallenges 37
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viii Contents
2.3.2.1 WhatanF2CProviderShouldBe? 38
2.3.2.2 ShallCloud/FogProvidersCommunicatewithEachOther 38
2.3.2.3 HowMultifog/CloudAccessIsManaged 39
2.3.3 BusinessChallenges 40
2.4 OngoingEfforts 41
2.4.1 ECC 41
2.4.2 mF2C 42
2.4.3 MEC 42
2.4.4 OEC 44
2.4.5 OFC 44
2.5 HandlingDatainCoordinatedScenarios 45
2.5.1 TheNewData 46
2.5.2 TheLifeCycleofData 48
2.5.3 F2CDataManagement 49
2.5.3.1 DataCollection 49
2.5.3.2 DataStorage 51
2.5.3.3 DataProcessing 52
2.6 TheComingFuture 52
Acknowledgments 54
References 54
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3 ComputationOffloadingGameforFog-CloudScenario 61
HamedShah-MansouriandVincentW.S.Wong
3.1 InternetofThings 61
3.2 FogComputing 63
3.2.1 OverviewofFogComputing 63
3.2.2 ComputationOffloading 64
3.2.2.1 EvaluationCriteria 65
3.2.2.2 LiteratureReview 66
3.3 AComputationTaskOffloadingGameforHybridFog-Cloud
Computing 67
3.3.1 SystemModel 67
3.3.1.1 HybridFog-CloudComputing 68
3.3.1.2 ComputationTaskModels 68
3.3.1.3 QualityofExperience 71
3.3.2 ComputationOffloadingGame 71
3.3.2.1 GameFormulation 71
3.3.2.2 AlgorithmDevelopment 74
3.3.2.3 PriceofAnarchy 74
3.3.2.4 PerformanceEvaluation 75
3.4 Conclusion 80
References 80
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Contents ix
4 PricingTradeoffsforDataAnalyticsinFog–Cloud
Scenarios 83
YichenRuan,LiangZheng,MariaGorlatova,MungChiang,and
CarleeJoe-Wong
4.1 Introduction:EconomicsandFogComputing 83
4.1.1 FogApplicationPricing 85
4.1.2 IncentivizingFogResources 86
4.1.3 AFogonomicsResearchAgenda 86
4.2 FogPricingToday 87
4.2.1 PricingNetworkResources 87
4.2.2 PricingComputingResources 89
4.2.3 PricingandArchitectureTrade-offs 89
4.3 TypicalFogArchitectures 90
4.3.1 FogApplications 90
4.3.2 TheCloud-to-ThingsContinuum 90
4.4 ACaseStudy:DistributedDataProcessing 92
4.4.1 ATemperatureSensorTestbed 92
4.4.2 Latency,Cost,andRisk 95
4.4.3 SystemTrade-off:FogorCloud 98
4.5 FutureResearchDirections 101
(cid:2) 4.6 Conclusion 102 (cid:2)
Acknowledgments 102
References 103
5 QuantitativeandQualitativeEconomicBenefitsofFog 107
JoeWeinman
5.1 CharacteristicsofFogComputingSolutions 108
5.2 StrategicValue 109
5.2.1 InformationExcellence 110
5.2.2 SolutionLeadership 110
5.2.3 CollectiveIntimacy 110
5.2.4 AcceleratedInnovation 111
5.3 Bandwidth,Latency,andResponseTime 111
5.3.1 NetworkLatency 113
5.3.2 ServerLatency 114
5.3.3 BalancingConsolidationandDispersiontoMinimizeTotal
Latency 114
5.3.4 DataTrafficVolume 115
5.3.5 NodesandInterconnections 116
5.4 Capacity,Utilization,Cost,andResourceAllocation 117
5.4.1 CapacityRequirements 117
5.4.2 CapacityUtilization 118
5.4.3 UnitCostofDeliveredResources 119
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5.4.4 ResourceAllocation,Sharing,andScheduling 120
5.5 InformationValueandServiceQuality 120
5.5.1 PrecisionandAccuracy 120
5.5.2 Survivability,Availability,andReliability 122
5.6 Sovereignty,Privacy,Security,Interoperability,and
Management 123
5.6.1 DataSovereignty 123
5.6.2 PrivacyandSecurity 123
5.6.3 HeterogeneityandInteroperability 124
5.6.4 Monitoring,Orchestration,andManagement 124
5.7 Trade-Offs 125
5.8 Conclusion 126
References 126
6 IncentiveSchemesforUser-ProvidedFog
Infrastructure 129
GeorgeIosifidis,LinGao,JianweiHuang,andLeandrosTassiulas
6.1 Introduction 129
6.2 TechnologyandEconomicIssuesinUPIs 132
6.2.1 OverviewofUPImodelsforNetworkConnectivity 132
(cid:2) 6.2.2 TechnicalChallengesofResourceAllocation 134 (cid:2)
6.2.3 IncentiveIssues 135
6.3 IncentiveMechanismsforAutonomousMobileUPIs 137
6.4 IncentiveMechanismsforProvider-assisted
MobileUPIs 140
6.5 IncentiveMechanismsforLarge-ScaleSystems 143
6.6 OpenChallengesinMobileUPIIncentiveMechanisms 145
6.6.1 AutonomousMobileUPIs 145
6.6.1.1 ConsensusoftheServiceProvider 145
6.6.1.2 DynamicSetting 146
6.6.2 Provider-assistedMobileUPIs 146
6.6.2.1 ModelingtheUsers 146
6.6.2.2 IncompleteMarketInformation 147
6.7 Conclusions 147
References 148
7 Fog-BasedServiceEnablementArchitecture 151
NanxiChen,SiobhánClarke,andShuChen
7.1 Introduction 151
7.1.1 ObjectivesandChallenges 152
7.2 OngoingEffortonFogSEA 153
7.2.1 FogSEAServiceDescription 156
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Contents xi
7.2.2 SemanticDataDependencyOverlayNetwork 158
7.2.2.1 CreationandMaintenance 159
7.2.2.2 Semantic-BasedServiceMatchmarking 161
7.3 EarlyResults 164
7.3.1 ServiceComposition 165
7.3.1.1 SeDDONCreationinFogSEA 167
7.3.2 RelatedWork 168
7.3.2.1 Semantic-BasedServiceOverlays 169
7.3.2.2 Goal-DrivenPlanning 170
7.3.2.3 ServiceDiscovery 171
7.3.3 OpenIssueandFutureWork 172
References 174
8 Software-DefinedFogOrchestrationforIoTServices 179
RenyuYang,ZhenyuWen,DavidMcKee,TaoLin,JieXu,and
PeterGarraghan
8.1 Introduction 179
8.2 ScenarioandApplication 182
8.2.1 ConceptDefinition 182
8.2.2 Fog-enabledIoTApplication 184
(cid:2) 8.2.3 CharacteristicsandOpenChallenges 185 (cid:2)
8.2.4 OrchestrationRequirements 187
8.3 Architecture:ASoftware-DefinedPerspective 188
8.3.1 SolutionOverview 188
8.3.2 Software-DefinedArchitecture 189
8.4 Orchestration 191
8.4.1 ResourceFilteringandAssignment 192
8.4.2 ComponentSelectionandPlacement 194
8.4.3 DynamicOrchestrationwithRuntimeQoS 195
8.4.4 SystematicData-DrivenOptimization 196
8.4.5 Machine-LearningforOrchestration 197
8.5 FogSimulation 198
8.5.1 Overview 198
8.5.2 SimulationforIoTApplicationinFog 199
8.5.3 SimulationforFogOrchestration 201
8.6 EarlyExperience 202
8.6.1 Simulation-BasedOrchestration 202
8.6.2 OrchestrationinContainer-BasedSystems 206
8.7 Discussion 207
8.8 Conclusion 208
Acknowledgment 208
References 208
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xii Contents
9 ADecentralizedAdaptationSystemforQoS
Optimization 213
NanxiChen,FanLi,GaryWhite,SiobhánClarke,andYangYang
9.1 Introduction 213
9.2 StateoftheArt 217
9.2.1 QoS-awareServiceComposition 217
9.2.2 SLA(Re-)negotiation 219
9.2.3 ServiceMonitoring 221
9.3 FogServiceDeliveryModelandAdaptFog 224
9.3.1 AdaptFogArchitecture 224
9.3.2 ServicePerformanceValidation 227
9.3.3 RuntimeQoSMonitoring 232
9.3.4 Fog-to-FogServiceLevelRenegotiation 235
9.4 ConclusionandOpenIssues 240
References 240
10 EfficientTaskSchedulingforPerformance
Optimization 249
YangYang,ShuangZhao,KunlunWang,andZeningLiu
10.1 Introduction 249
(cid:2) 10.2 IndividualDelay-minimizationTaskScheduling 251 (cid:2)
10.2.1 SystemModel 251
10.2.2 ProblemFormulation 251
10.2.3 POMTAlgorithm 253
10.3 Energy-efficientTaskScheduling 255
10.3.1 FogComputingNetwork 255
10.3.2 MediumAccessProtocol 257
10.3.3 EnergyEfficiency 257
10.3.4 ProblemProperties 258
10.3.5 OptimalTaskSchedulingStrategy 259
10.4 DelayEnergyBalancedTaskScheduling 260
10.4.1 OverviewofHomogeneousFogNetworkModel 260
10.4.2 ProblemFormulationandAnalyticalFramework 261
10.4.3 DelayEnergyBalancedTaskOffloading 262
10.4.4 PerformanceAnalysis 262
10.5 OpenChallengesinTaskScheduling 265
10.5.1 HeterogeneityofMobileNodes 265
10.5.2 MobilityofMobileNodes 265
10.5.3 JointTaskandTrafficScheduling 265
10.6 Conclusion 266
References 266
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