Fog and fogonomics : challenges and practices of fog computing, communication, networking, strategy, and economics / edited by Yang Yang, ShanghaiTech University, Shanghai, China, Jianwei Huang, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China, Tao Zhang, Futurwei Technologies, New Jersey, USA, Joe Weinman, XFORMA LLC, Flanders, NJ, USA.
By: Yang, Yang (Professor at ShanghaiTech) [author.]
Language: English Series: Information and communication technology seriesPublisher: Hoboken, New Jersey : John Wiley & Sons, Inc., 2020Edition: First editionDescription: 1 online resource (xxi, 375 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9781119501114Subject(s): Cloud computingGenre/Form: Electronic booksDDC classification: 004.6782 LOC classification: QA76.585Online resources: Full text available at Wiley Online Library Click here to view| Item type | Current location | Home library | Call number | Copy number | Status | Date due | Barcode | Item holds |
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| 004.62 J958 2002 Juniper Networks routers : the complete reference / | 004.678 B917 1999 Intronet : a beginner's guide to searching the Internet / | 004.6782 Se266 2019 Security, privacy and digital forensics in the cloud / | 004.6782 Y168 2020 Fog and fogonomics : challenges and practices of fog computing, communication, networking, strategy, and economics / | 004.6782 Y168 2020 Fog and fogonomics : challenges and practices of fog computing, communication, networking, strategy, and economics / | 004.68 F497 2008 Voice over WLANs : the complete guide / | 004.695 Ic7 1993 Icodata fax modem user's manual / |
Includes bibliographical references and index.
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
"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"-- Provided by publisher.
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