List of Contributors xvii Preface xxi 1 Fog Computing and Fogonomics 1Yang Yang, Jianwei Huang, Tao Zhang, and Joe Weinman 2 Collaborative Mechanism for Hybrid Fog-Cloud Scenarios 7Xavi 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 61Hamed 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 83Yichen 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 107Joe 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 129George 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 151Nanxi 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 179Renyu 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 213Nanxi 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 249Yang 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 269Xu 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 295Jaeyoon 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 325Yuan-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 353Mithun 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

THE ONE-STOP RESOURCE FOR ANY INDIVIDUAL OR ORGANIZATION CONSIDERING FOG COMPUTING Fog and Fogonomics is a comprehensive and technology-centric resource that highlights the system model, architectures, building blocks, and IEEE standards for fog computing platforms and solutions. The "fog" is defined as the multiple interconnected layers of computing along the continuum from cloud to endpoints such as user devices and things including racks or microcells in server closets, residential gateways, factory control systems, and more. The authors—noted experts on the topic—review business models and metrics that allow for the economic assessment of fog-based information communication technology (ICT) resources, especially mobile resources. The book contains a wide range of templates and formulas for calculating quality-of-service values. Comprehensive in scope, it covers topics including fog computing technologies and reference architecture, fog-related standards and markets, fog-enabled applications and services, fog economics (fogonomics), and strategy. This important resource: Offers a comprehensive text on fog computingDiscusses pricing, service level agreements, service delivery, and consumption of fog computingExamines how fog has the potential to change the information and communication technology industry in the next decadeDescribes how fog enables new business models, strategies, and competitive differentiation, as with ecosystems of connected and smart digital products and servicesIncludes case studies featuring integration of fog computing, communication, and networking systems Written for product and systems engineers and designers, as well as for faculty and students, Fog and Fogonomics is an essential book that explores the technological and economic issues associated with fog computing.