Fog and fogonomics : challenges and practices of fog computing, communication, networking, strategy, and economics / / edited by Yang Yang, Jianwei Huang, Tao Zhang, Joe Weinman

• Record Nr.: UNINA9910823222803321
• Autore: Yang Yang (Professor at ShanghaiTech)
• Pubbl/distr/stampa: Hoboken, New Jersey : , : John Wiley & Sons, Inc., , 2020; [Piscataqay, New Jersey] : , : IEEE Xplore, , [2020]
• ISBN: 1-119-50111-3; 1-119-50112-1; 1-119-50110-5
• Descrizione fisica: 1 online resource (419 pages)
• Collana: Information and communication technology series
• Disciplina: 004.6782
• Soggetti: Cloud computing
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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.
• Sommario/riassunto: "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"--