Fog and fogonomics : challenges and practices of fog computing, communication, networking, strategy, and economics

List of Contributors xvii

Preface xxi

1 Fog Computing and Fogonomics 1
Yang Yang, Jianwei Huang, Tao Zhang, and Joe Weinman

2 Collaborative Mechanism for Hybrid Fog-Cloud Scenarios 7
Xavi Masip, Eva Marín, Jordi Garcia, and Sergi Sànchez

2.1 The Collaborative Scenario 7

2.1.1 The F2C Model 11

2.1.1.1 The Layering Architecture 13

2.1.1.2 The Fog Node 14

2.1.1.3 F2C as a Service 16

2.1.2 The F2C Control Architecture 19

2.1.2.1 Hierarchical Architecture 20

2.1.2.2 Main Functional Blocks 24

2.1.2.3 Managing Control Data 25

2.1.2.4 Sharing Resources 26

2.2 Benefits and Applicability 28

2.3 The Challenges 29

2.3.1 Research Challenges 30

2.3.1.1 What a Resource is 30

2.3.1.2 Categorization 30

2.3.1.3 Identification 31

2.3.1.4 Clustering 33

2.3.1.5 Resources Discovery 33

2.3.1.6 Resource Allocation 34

2.3.1.7 Reliability 35

2.3.1.8 QoS 36

2.3.1.9 Security 36

2.3.2 Industry Challenges 37

2.3.2.1 What an F2C Provider Should Be? 38

2.3.2.2 Shall Cloud/Fog Providers Communicate with Each Other 38

2.3.2.3 How Multifog/Cloud Access is Managed 39

2.3.3 Business Challenges 40

2.4 Ongoing Efforts 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 Handling Data in Coordinated Scenarios 45

2.5.1 The New Data 46

2.5.2 The Life Cycle of Data 48

2.5.3 F2C Data Management 49

2.5.3.1 Data Collection 49

2.5.3.2 Data Storage 51

2.5.3.3 Data Processing 52

2.6 The Coming Future 52

Acknowledgments 54

References 54

3 Computation Offloading Game for Fog-Cloud Scenario 61
Hamed Shah-Mansouri and Vincent W.S. Wong

3.1 Internet of Things 61

3.2 Fog Computing 63

3.2.1 Overview of Fog Computing 63

3.2.2 Computation Offloading 64

3.2.2.1 Evaluation Criteria 65

3.2.2.2 Literature Review 66

3.3 A Computation Task Offloading Game for Hybrid Fog-Cloud Computing 67

3.3.1 System Model 67

3.3.1.1 Hybrid Fog-Cloud Computing 68

3.3.1.2 Computation Task Models 68

3.3.1.3 Quality of Experience 71

3.3.2 Computation Offloading Game 71

3.3.2.1 Game Formulation 71

3.3.2.2 Algorithm Development 74

3.3.2.3 Price of Anarchy 74

3.3.2.4 Performance Evaluation 75

3.4 Conclusion 80

References 80

4 Pricing Tradeoffs for Data Analytics in Fog–Cloud Scenarios 83
Yichen Ruan, Liang Zheng, Maria Gorlatova, Mung Chiang, and Carlee Joe-Wong

4.1 Introduction: Economics and Fog Computing 83

4.1.1 Fog Application Pricing 85

4.1.2 Incentivizing Fog Resources 86

4.1.3 A Fogonomics Research Agenda 86

4.2 Fog Pricing Today 87

4.2.1 Pricing Network Resources 87

4.2.2 Pricing Computing Resources 89

4.2.3 Pricing and Architecture Trade-offs 89

4.3 Typical Fog Architectures 90

4.3.1 Fog Applications 90

4.3.2 The Cloud-to-Things Continuum 90

4.4 A Case Study: Distributed Data Processing 92

4.4.1 A Temperature Sensor Testbed 92

4.4.2 Latency, Cost, and Risk 95

4.4.3 System Trade-off: Fog or Cloud 98

4.5 Future Research Directions 101

4.6 Conclusion 102

Acknowledgments 102

References 103

5 Quantitative and Qualitative Economic Benefits of Fog 107
Joe Weinman

5.1 Characteristics of Fog Computing Solutions 108

5.2 Strategic Value 109

5.2.1 Information Excellence 110

5.2.2 Solution Leadership 110

5.2.3 Collective Intimacy 110

5.2.4 Accelerated Innovation 111

5.3 Bandwidth, Latency, and Response Time 111

5.3.1 Network Latency 113

5.3.2 Server Latency 114

5.3.3 Balancing Consolidation and Dispersion to Minimize Total Latency 114

5.3.4 Data Traffic Volume 115

5.3.5 Nodes and Interconnections 116

5.4 Capacity, Utilization, Cost, and Resource Allocation 117

5.4.1 Capacity Requirements 117

5.4.2 Capacity Utilization 118

5.4.3 Unit Cost of Delivered Resources 119

5.4.4 Resource Allocation, Sharing, and Scheduling 120

5.5 Information Value and Service Quality 120

5.5.1 Precision and Accuracy 120

5.5.2 Survivability, Availability, and Reliability 122

5.6 Sovereignty, Privacy, Security, Interoperability, and Management 123

5.6.1 Data Sovereignty 123

5.6.2 Privacy and Security 123

5.6.3 Heterogeneity and Interoperability 124

5.6.4 Monitoring, Orchestration, and Management 124

5.7 Trade-Offs 125

5.8 Conclusion 126

References 126

6 Incentive Schemes for User-Provided Fog Infrastructure 129
George Iosifidis, Lin Gao, Jianwei Huang, and Leandros Tassiulas

6.1 Introduction 129

6.2 Technology and Economic Issues in UPIs 132

6.2.1 Overview of UPI models for Network Connectivity 132

6.2.2 Technical Challenges of Resource Allocation 134

6.2.3 Incentive Issues 135

6.3 Incentive Mechanisms for Autonomous Mobile UPIs 137

6.4 Incentive Mechanisms for Provider-assisted Mobile UPIs 140

6.5 Incentive Mechanisms for Large-Scale Systems 143

6.6 Open Challenges in Mobile UPI Incentive Mechanisms 145

6.6.1 Autonomous Mobile UPIs 145

6.6.1.1 Consensus of the Service Provider 145

6.6.1.2 Dynamic Setting 146

6.6.2 Provider-assisted Mobile UPIs 146

6.6.2.1 Modeling the Users 146

6.6.2.2 Incomplete Market Information 147

6.7 Conclusions 147

References 148

7 Fog-Based Service Enablement Architecture 151
Nanxi Chen, Siobhán Clarke, and Shu Chen

7.1 Introduction 151

7.1.1 Objectives and Challenges 152

7.2 Ongoing Effort on FogSEA 153

7.2.1 FogSEA Service Description 156

7.2.2 Semantic Data Dependency Overlay Network 158

7.2.2.1 Creation and Maintenance 159

7.2.2.2 Semantic-Based Service Matchmarking 161

7.3 Early Results 164

7.3.1 Service Composition 165

7.3.1.1 SeDDON Creation in FogSEA 167

7.3.2 Related Work 168

7.3.2.1 Semantic-Based Service Overlays 169

7.3.2.2 Goal-Driven Planning 170

7.3.2.3 Service Discovery 171

7.3.3 Open Issue and Future Work 172

References 174

8 Software-Defined Fog Orchestration for IoT Services 179
Renyu Yang, Zhenyu Wen, David McKee, Tao Lin, Jie Xu, and Peter Garraghan

8.1 Introduction 179

8.2 Scenario and Application 182

8.2.1 Concept Definition 182

8.2.2 Fog-enabled IoT Application 184

8.2.3 Characteristics and Open Challenges 185

8.2.4 Orchestration Requirements 187

8.3 Architecture: A Software-Defined Perspective 188

8.3.1 Solution Overview 188

8.3.2 Software-Defined Architecture 189

8.4 Orchestration 191

8.4.1 Resource Filtering and Assignment 192

8.4.2 Component Selection and Placement 194

8.4.3 Dynamic Orchestration with Runtime QoS 195

8.4.4 Systematic Data-Driven Optimization 196

8.4.5 Machine-Learning for Orchestration 197

8.5 Fog Simulation 198

8.5.1 Overview 198

8.5.2 Simulation for IoT Application in Fog 199

8.5.3 Simulation for Fog Orchestration 201

8.6 Early Experience 202

8.6.1 Simulation-Based Orchestration 202

8.6.2 Orchestration in Container-Based Systems 206

8.7 Discussion 207

8.8 Conclusion 208

Acknowledgment 208

References 208

9 A Decentralized Adaptation System for QoS Optimization 213
Nanxi Chen, Fan Li, Gary White, Siobhán Clarke, and Yang Yang

9.1 Introduction 213

9.2 State of the Art 217

9.2.1 QoS-aware Service Composition 217

9.2.2 SLA (Re-)negotiation 219

9.2.3 Service Monitoring 221

9.3 Fog Service Delivery Model and AdaptFog 224

9.3.1 AdaptFog Architecture 224

9.3.2 Service Performance Validation 227

9.3.3 Runtime QoS Monitoring 232

9.3.4 Fog-to-Fog Service Level Renegotiation 235

9.4 Conclusion and Open Issues 240

References 240

10 Efficient Task Scheduling for Performance Optimization 249
Yang Yang, Shuang Zhao, Kunlun Wang, and Zening Liu

10.1 Introduction 249

10.2 Individual Delay-minimization Task Scheduling 251

10.2.1 System Model 251

10.2.2 Problem Formulation 251

10.2.3 POMT Algorithm 253

10.3 Energy-efficient Task Scheduling 255

10.3.1 Fog Computing Network 255

10.3.2 Medium Access Protocol 257

10.3.3 Energy Efficiency 257

10.3.4 Problem Properties 258

10.3.5 Optimal Task Scheduling Strategy 259

10.4 Delay Energy Balanced Task Scheduling 260

10.4.1 Overview of Homogeneous Fog Network Model 260

10.4.2 Problem Formulation and Analytical Framework 261

10.4.3 Delay Energy Balanced Task Offloading 262

10.4.4 Performance Analysis 262

10.5 Open Challenges in Task Scheduling 265

10.5.1 Heterogeneity of Mobile Nodes 265

10.5.2 Mobility of Mobile Nodes 265

10.5.3 Joint Task and Traffic Scheduling 265

10.6 Conclusion 266

References 266

11 Noncooperative and Cooperative Computation Offloading 269
Xu Chen and Zhi Zhou

11.1 Introduction 269

11.2 Related Works 271

11.3 Noncooperative Computation Offloading 272

11.3.1 System Model 272

11.3.1.1 Communication Model 272

11.3.1.2 Computation Model 273

11.3.2 Decentralized Computation Offloading Game 275

11.3.2.1 Game Formulation 275

11.3.2.2 Game Property 276

11.3.3 Decentralized Computation Offloading Mechanism 280

11.3.3.1 Mechanism Design 280

11.3.3.2 Performance Analysis 282

11.4 Cooperative Computation Offloading 283

11.4.1 HyFog Framework Model 283

11.4.1.1 Resource Model 283

11.4.1.2 Task Execution Model 284

11.4.2 Inadequacy of Bipartite Matching–Based Task Offloading 285

11.4.3 Three-Layer Graph Matching Based Task Offloading 287

11.5 Discussions 289

11.5.1 Incentive Mechanisms for Collaboration 290

11.5.2 Coping with System Dynamics 290

11.5.3 Hybrid Centralized–Decentralized Implementation 291

11.6 Conclusion 291

References 292

12 A Highly Available Storage System for Elastic Fog 295
Jaeyoon Chung, Carlee Joe-Wong, and Sangtae Ha

12.1 Introduction 295

12.1.1 Fog Versus Cloud Services 296

12.1.2 A Fog Storage Service 297

12.2 Design 299

12.2.1 Design Considerations 299

12.2.2 Architecture 300

12.2.3 File Operations 301

12.3 Fault Tolerant Data Access and Share Placement 303

12.3.1 Data Encoding and Placement Scheme 303

12.3.2 Robust and Exact Share Requests 304

12.3.3 Clustering Storage Nodes 305

12.3.4 Storage Selection 306

12.3.4.1 File Download Times 307

12.3.4.2 Optimizing Share Locations 307

12.4 Implementation 309

12.4.1 Metadata 310

12.4.2 Access Counting 311

12.4.3 NAT Traversal 312

12.5 Evaluation 312

12.6 Discussion and Open Questions 318

12.7 Related Work 319

12.8 Conclusion 320

Acknowledgments 320

References 320

13 Development of Wearable Services with Edge Devices 325
Yuan-Yao Shih, Ai-Chun Pang, and Yuan-Yao Lou

13.1 Introduction 325

13.2 Related Works 328

13.2.1 Without Developer’s Effort 329

13.2.2 Require Developer’s Effort 330

13.3 Problem Description 331

13.4 System Architecture 332

13.4.1 End Device 332

13.4.2 Fog Node 333

13.4.3 Controller 333

13.5 Methodology 333

13.5.1 End Device 334

13.5.1.1 Localization 334

13.5.1.2 Speech Recognition 335

13.5.1.3 Retrieving Google Calendar Information 336

13.5.2 Fog Node 337

13.5.3 Controller 338

13.6 Performance Evaluation 339

13.6.1 Experiment Setup 339

13.6.2 Different Computation Loads 340

13.6.3 Different Types of Applications 342

13.6.4 Remote Wearable Services Provision 344

13.6.5 Estimation of Power Consumption 346

13.7 Discussion 348

13.8 Conclusion 349

References 350

14 Security and Privacy Issues and Solutions for Fog 353
Mithun Mukherjee, Mohamed Amine Ferrag, Leandros Maglaras, Abdelouahid Derhab, and Mohammad Aazam

14.1 Introduction 353

14.1.1 Major Limitations in Traditional Cloud Computing 353

14.1.2 Fog Computing: An Edge Computing Paradigm 354

14.1.3 A Three-Tier Fog Computing Architecture 357

14.2 Security and Privacy Challenges Posed by Fog Computing 360

14.3 Existing Research on Security and Privacy Issues in Fog Computing 361

14.3.1 Privacy-preserving 361

14.3.2 Authentication 363

14.3.3 Access Control 363

14.3.4 Malicious attacks 364

14.4 Open Questions and Research Challenges 366

14.4.1 Trust 367

14.4.2 Privacy preservation 367

14.4.3 Authentication 367

14.4.4 Malicious Attacks and Intrusion Detection 368

14.4.5 Cross-border Issues and Fog Forensic 369

14.5 Summary 369

Exercises 370

References 370

Index 375