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Fog and fogonomics : challenges and practices of fog computing, communication, networking, strategy, and economics
- Author / Creator
- Available as
- Online
- Summary
-
"Recent industry surveys expect the quantity of connected devices and sensors to be in excess of 50 billion worldwide by 2020, and these devices can generate huge amounts of data every single day. ...
"Recent industry surveys expect the quantity of connected devices and sensors to be in excess of 50 billion worldwide by 2020, and these devices can generate huge amounts of data every single day. It becomes a big challenge to analyze and create actionable information from the data. Fog computing, as a promising solution to extend the capability of Clouds, has attracted considerable attention. Fogs are lightweight distributed technology platforms diffused among end-user devices in wired and wireless networks to provide highly scalable and resilient environments that can be utilized by organizations in a multitude of ways. Discusses pricing, service level agreements, service delivery, and consumption of fog computing Examines how fog will change the information and communication technology industry in the next decade . Describes how fog enables new business models, strategies, and competitive differentiation, as with ecosystems of connected, smart, digital products and services"--
Details
- Creator
- edited by Yang Yang, Jianwei Huang, Tao Zhang, Joe Weinman
- Format
- Books
- Language
- English
- Contributors
- Publication
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- Hoboken, New Jersey : John Wiley & Sons, Inc., 2020
- [Piscataqay, New Jersey] : IEEE Xplore, [2020]
- Physical Details
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- 1 online resource (419 pages)
- ISBNs
- 9781119501114, 1119501121, 9781119501091, 1119501113, 9781119501121, 9781119501107, 1119501105, 1119501091
- OCLC
- on1138030139
- Includes bibliographical references and index.
- Also available in print.
- 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 Mar℗<U+0083>in, Jordi Garcia, and Sergi S© nchez -- 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 Foǵ<U+0093>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, Siobh©Łn 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, Siobh©Łn 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 Matchinǵ<U+0093>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 Centralized́<U+0093>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 Developeŕ<U+0099>s Effort 329 -- 13.2.2 Require Developeŕ<U+0099>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