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Fog and Fogonomics: Challenges and Practices of Fog Computing, Communication, Networking, Strategy, and Economics [Kõva köide]

Edited by , Edited by (Shanghai Institute of Fog Computing Technology, China), Edited by (National Institute of Standards and Technology, Gaithersburg, MD, USA), Edited by (Chinese University of Hong Kong, Shenzhen, China)
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Fog and Fogonomics: Challenges and Practices of Fog Computing, Communication, Networking, Strategy, and EconomicsSuurem pilt
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Püsilink: https://www.kriso.ee/db/9781119501091.html
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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 computing
  • Discusses pricing, service level agreements, service delivery, and consumption of fog computing
  • Examines how fog has the potential to 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 and smart digital products and services
  • Includes 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.

List of Contributors xvii
Preface xxi
1 Fog Computing and Fogonomics 1(6)
Yang Yang
Jianwei Huang
Tao Zhang
Joe Weinman
2 Collaborative Mechanism for Hybrid Fog-Cloud Scenarios 7(54)
Xavi Masip
Eva Marin
Jordi Garcia
Sergi Sanchez
2.1 The Collaborative Scenario
7(21)
2.1.1 The F2C Model
11(8)
2.1.1.1 The Layering Architecture
13(1)
2.1.1.2 The Fog Node
14(2)
2.1.1.3 F2C as a Service
16(3)
2.1.2 The F2C Control Architecture
19(10)
2.1.2.1 Hierarchical Architecture
20(4)
2.1.2.2 Main Functional Blocks
24(1)
2.1.2.3 Managing Control Data
25(1)
2.1.2.4 Sharing Resources
26(2)
2.2 Benefits and Applicability
28(1)
2.3 The Challenges
29(12)
2.3.1 Research Challenges
30(7)
2.3.1.1 What a Resource is
30(1)
2.3.1.2 Categorization
30(1)
2.3.1.3 Identification
31(2)
2.3.1.4 Clustering
33(1)
2.3.1.5 Resources Discovery
33(1)
2.3.1.6 Resource Allocation
34(1)
2.3.1.7 Reliability
35(1)
2.3.1.8 QoS
36(1)
2.3.1.9 Security
36(1)
2.3.2 Industry Challenges
37(3)
2.3.2.1 What an F2C Provider Should Be?
38(1)
2.3.2.2 Shall Cloud/Fog Providers Communicate with Each Other
38(1)
2.3.2.3 How Multifog/Cloud Access Is Managed
39(1)
2.3.3 Business Challenges
40(1)
2.4 Ongoing Efforts
41(4)
2.4.1 ECC
41(1)
2.4.2 mF2C
42(1)
2.4.3 MEC
42(2)
2.4.4 OEC
44(1)
2.4.5 OFC
44(1)
2.5 Handling Data in Coordinated Scenarios
45(7)
2.5.1 The New Data
46(2)
2.5.2 The Life Cycle of Data
48(1)
2.5.3 F2C Data Management
49(14)
2.5.3.1 Data Collection
49(2)
2.5.3.2 Data Storage
51(1)
2.5.3.3 Data Processing
52(1)
2.6 The Coming Future
52(2)
Acknowledgments
54(1)
References
54(7)
3 Computation Offloading Game for Fog-Cloud Scenario 61(22)
Homed Shah-Mansouri
Vincent W.S. Wong
3.1 Internet of Things
61(2)
3.2 Fog Computing
63(4)
3.2.1 Overview of Fog Computing
63(1)
3.2.2 Computation Offloading
64(3)
3.2.2.1 Evaluation Criteria
65(1)
3.2.2.2 Literature Review
66(1)
3.3 A Computation Task Offloading Game for Hybrid Fog-Cloud Computing
67(13)
3.3.1 System Model
67(4)
3.3.1.1 Hybrid Fog-Cloud Computing
68(1)
3.3.1.2 Computation Task Models
68(3)
3.3.1.3 Quality of Experience
71(1)
3.3.2 Computation Offloading Game
71(12)
3.3.2.1 Game Formulation
71(3)
3.3.2.2 Algorithm Development
74(1)
3.3.2.3 Price of Anarchy
74(1)
3.3.2.4 Performance Evaluation
75(5)
3.4 Conclusion
80(1)
References
80(3)
4 Pricing Tradeoffs for Data Analytics in Fog-Cloud Scenarios 83(24)
Iichen Ruan
Liang Zheng
Maria Gorlatova
Mung Chiang
Carlee Joe-Wang
4.1 Introduction: Economics and Fog Computing
83(4)
4.1.1 Fog Application Pricing
85(1)
4.1.2 Incentivizing Fog Resources
86(1)
4.1.3 A Fogonomics Research Agenda
86(1)
4.2 Fog Pricing Today
87(3)
4.2.1 Pricing Network Resources
87(2)
4.2.2 Pricing Computing Resources
89(1)
4.2.3 Pricing and Architecture Trade-offs
89(1)
4.3 Typical Fog Architectures
90(2)
4.3.1 Fog Applications
90(1)
4.3.2 The Cloud-to-Things Continuum
90(2)
4.4 A Case Study: Distributed Data Processing
92(9)
4.4.1 A Temperature Sensor Testbed
92(3)
4.4.2 Latency, Cost, and Risk
95(3)
4.4.3 System Trade-off: Fog or Cloud
98(3)
4.5 Future Research Directions
101(1)
4.6 Conclusion
102(1)
Acknowledgments
102(1)
References
103(4)
5 Quantitative and Qualitative Economic Benefits of Fog 107(22)
Joe Weinman
5.1 Characteristics of Fog Computing Solutions
108(1)
5.2 Strategic Value
109(2)
5.2.1 Information Excellence
110(1)
5.2.2 Solution Leadership
110(1)
5.2.3 Collective Intimacy
110(1)
5.2.4 Accelerated Innovation
111(1)
5.3 Bandwidth, Latency, and Response Time
111(6)
5.3.1 Network Latency
113(1)
5.3.2 Server Latency
114(1)
5.3.3 Balancing Consolidation and Dispersion to Minimize Total Latency
114(1)
5.3.4 Data Traffic Volume
115(1)
5.3.5 Nodes and Interconnections
116(1)
5.4 Capacity, Utilization, Cost, and Resource Allocation
117(3)
5.4.1 Capacity Requirements
117(1)
5.4.2 Capacity Utilization
118(1)
5.4.3 Unit Cost of Delivered Resources
119(1)
5.4.4 Resource Allocation, Sharing, and Scheduling
120(1)
5.5 Information Value and Service Quality
120(3)
5.5.1 Precision and Accuracy
120(2)
5.5.2 Survivability, Availability, and Reliability
122(1)
5.6 Sovereignty, Privacy, Security, Interoperability, and Management
123(2)
5.6.1 Data Sovereignty
123(1)
5.6.2 Privacy and Security
123(1)
5.6.3 Heterogeneity and Interoperability
124(1)
5.6.4 Monitoring, Orchestration, and Management
124(1)
5.7 Trade-Offs
125(1)
5.8 Conclusion
126(1)
References
126(3)
6 Incentive Schemes for User-Provided Fog Infrastructure 129(22)
George Iosifidis
Lin Gao
Jianwei Huang
Leandros Tassiulas
6.1 Introduction
129(3)
6.2 Technology and Economic Issues in UPIs
132(5)
6.2.1 Overview of UPI models for Network Connectivity
132(2)
6.2.2 Technical Challenges of Resource Allocation
134(1)
6.2.3 Incentive Issues
135(2)
6.3 Incentive Mechanisms for Autonomous Mobile UPIs
137(3)
6.4 Incentive Mechanisms for Provider-assisted Mobile UPIs
140(3)
6.5 Incentive Mechanisms for Large-Scale Systems
143(2)
6.6 Open Challenges in Mobile UPI Incentive Mechanisms
145(2)
6.6.1 Autonomous Mobile UPIs
145(1)
6.6.1.1 Consensus of the Service Provider
145(1)
6.6.1.2 Dynamic Setting
146(1)
6.6.2 Provider-assisted Mobile UPIs
146(5)
6.6.2.1 Modeling the Users
146(1)
6.6.2.2 Incomplete Market Information
147(1)
6.7 Conclusions
147(1)
References
148(3)
7 Fog-Based Service Enablement Architecture 151(28)
Nanxi Chen
Siobhcfn Clarke
Shu Chen
7.1 Introduction
151(2)
7.1.1 Objectives and Challenges
152(1)
7.2 Ongoing Effort on FogSEA
153(11)
7.2.1 FogSEA Service Description
156(2)
7.2.2 Semantic Data Dependency Overlay Network
158(6)
7.2.2.1 Creation and Maintenance
159(2)
7.2.2.2 Semantic-Based Service Matchmarking
161(3)
7.3 Early Results
164(10)
7.3.1 Service Composition
165(3)
7.3.1.1 SeDDON Creation in FogSEA
167(1)
7.3.2 Related Work
168(4)
7.3.2.1 Semantic-Based Service Overlays
169(1)
7.3.2.2 Goal-Driven Planning
170(1)
7.3.2.3 Service Discovery
171(1)
7.3.3 Open Issue and Future Work
172(2)
References
174(5)
8 Software-Defined Fog Orchestration for loT Services 179(34)
Renyu Yang
Zhenyu Wen
David McKee
Tao Lin
Jie Xu
Peter Garraghan
8.1 Introduction
179(3)
8.2 Scenario and Application
182(6)
8.2.1 Concept Definition
182(2)
8.2.2 Fog-enabled IoT Application
184(1)
8.2.3 Characteristics and Open Challenges
185(2)
8.2.4 Orchestration Requirements
187(1)
8.3 Architecture: A Software-Defined Perspective
188(3)
8.3.1 Solution Overview
188(1)
8.3.2 Software-Defined Architecture
189(2)
8.4 Orchestration
191(7)
8.4.1 Resource Filtering and Assignment
192(2)
8.4.2 Component Selection and Placement
194(1)
8.4.3 Dynamic Orchestration with Runtime QoS
195(1)
8.4.4 Systematic Data-Driven Optimization
196(1)
8.4.5 Machine-Learning for Orchestration
197(1)
8.5 Fog Simulation
198(4)
8.5.1 Overview
198(1)
8.5.2 Simulation for IoT Application in Fog
199(2)
8.5.3 Simulation for Fog Orchestration
201(1)
8.6 Early Experience
202(5)
8.6.1 Simulation-Based Orchestration
202(4)
8.6.2 Orchestration in Container-Based Systems
206(1)
8.7 Discussion
207(1)
8.8 Conclusion 208 Acknowledgment
208(1)
References
208(5)
9 A Decentralized Adaptation System for QoS Optimization 213(36)
Nanxi Chen
Fan Li
Gary White
Siobhan Clarke
Yang Yang
9.1 Introduction
213(4)
9.2 State of the Art
217(7)
9.2.1 QoS-aware Service Composition
217(2)
9.2.2 SLA (Re-)negotiation
219(2)
9.2.3 Service Monitoring
221(3)
9.3 Fog Service Delivery Model and AdaptFog
224(16)
9.3.1 AdaptFog Architecture
224(3)
9.3.2 Service Performance Validation
227(5)
9.3.3 Runtime QoS Monitoring
232(3)
9.3.4 Fog-to-Fog Service Level Renegotiation
235(5)
9.4 Conclusion and Open Issues
240(1)
References
240(9)
10 Efficient Task Scheduling for Performance Optimization 249(20)
Yang Yang
Shuang Zhao
Kunlun Wang
Zening Liu
10.1 Introduction
249(2)
10.2 Individual Delay-minimization Task Scheduling
251(4)
10.2.1 System Model
251(1)
10.2.2 Problem Formulation
251(2)
10.2.3 POMT Algorithm
253(2)
10.3 Energy-efficient Task Scheduling
255(5)
10.3.1 Fog Computing Network
255(2)
10.3.2 Medium Access Protocol
257(1)
10.3.3 Energy Efficiency
257(1)
10.3.4 Problem Properties
258(1)
10.3.5 Optimal Task Scheduling Strategy
259(1)
10.4 Delay Energy Balanced Task Scheduling
260(5)
10.4.1 Overview of Homogeneous Fog Network Model
260(1)
10.4.2 Problem Formulation and Analytical Framework
261(1)
10.4.3 Delay Energy Balanced Task Offloading
262(1)
10.4.4 Performance Analysis
262(3)
10.5 Open Challenges in Task Scheduling
265(1)
10.5.1 Heterogeneity of Mobile Nodes
265(1)
10.5.2 Mobility of Mobile Nodes
265(1)
10.5.3 Joint Task and Traffic Scheduling
265(1)
10.6 Conclusion
266(1)
References
266(3)
11 Noncooperative and Cooperative Computation Offloading 269(26)
Xu Chen
Zhi Zhou
11.1 Introduction
269(2)
11.2 Related Works
271(1)
11.3 Noncooperative Computation Offloading
272(11)
11.3.1 System Model
272(3)
11.3.1.1 Communication Model
272(1)
11.3.1.2 Computation Model
273(2)
11.3.2 Decentralized Computation Offloading Game
275(5)
11.3.2.1 Game Formulation
275(1)
11.3.2.2 Game Property
276(4)
11.3.3 Decentralized Computation Offloading Mechanism
280(3)
11.3.3.1 Mechanism Design
280(2)
11.3.3.2 Performance Analysis
282(1)
11.4 Cooperative Computation Offloading
283(6)
11.4.1 HyFog Framework Model
283(2)
11.4.1.1 Resource Model
283(1)
11.4.1.2 Task Execution Model
284(1)
11.4.2 Inadequacy of Bipartite Matching-Based Task Offloading
285(2)
11.4.3 Three-Layer Graph Matching Based Task Offloading
287(2)
11.5 Discussions
289(2)
11.5.1 Incentive Mechanisms for Collaboration
290(1)
11.5.2 Coping with System Dynamics
290(1)
11.5.3 Hybrid Centralized-Decentralized Implementation
291(1)
11.6 Conclusion
291(1)
References
292(3)
12 A Highly Available Storage System for Elastic Fog 295(30)
Jaeyoon Chung
Carlee Joe-Wong
Sangtae Ha
12.1 Introduction
295(4)
12.1.1 Fog Versus Cloud Services
296(1)
12.1.2 A Fog Storage Service
297(2)
12.2 Design
299(4)
12.2.1 Design Considerations
299(1)
12.2.2 Architecture
300(1)
12.2.3 File Operations
301(2)
12.3 Fault Tolerant Data Access and Share Placement
303(6)
12.3.1 Data Encoding and Placement Scheme
303(1)
12.3.2 Robust and Exact Share Requests
304(1)
12.3.3 Clustering Storage Nodes
305(1)
12.3.4 Storage Selection
306(3)
12.3.4.1 File Download Times
307(1)
12.3.4.2 Optimizing Share Locations
307(2)
12.4 Implementation
309(3)
12.4.1 Metadata
310(1)
12.4.2 Access Counting
311(1)
12.4.3 NAT Traversal
312(1)
12.5 Evaluation
312(6)
12.6 Discussion and Open Questions
318(1)
12.7 Related Work
319(1)
12.8 Conclusion
320(1)
Acknowledgments
320(1)
References
320(5)
13 Development of Wearable Services with Edge Devices 325(28)
Yuan-Yao Shih
Ai-Chun Pang
Yuan-Yao Lou
13.1 Introduction
325(3)
13.2 Related Works
328(3)
13.2.1 Without Developer's Effort
329(1)
13.2.2 Require Developer's Effort
330(1)
13.3 Problem Description
331(1)
13.4 System Architecture
332(1)
13.4.1 End Device
332(1)
13.4.2 Fog Node
333(1)
13.4.3 Controller
333(1)
13.5 Methodology
333(6)
13.5.1 End Device
334(3)
13.5.1.1 Localization
334(1)
13.5.1.2 Speech Recognition
335(1)
13.5.1.3 Retrieving Google Calendar Information
336(1)
13.5.2 Fog Node
337(1)
13.5.3 Controller
338(1)
13.6 Performance Evaluation
339(9)
13.6.1 Experiment Setup
339(1)
13.6.2 Different Computation Loads
340(2)
13.6.3 Different Types of Applications
342(2)
13.6.4 Remote Wearable Services Provision
344(2)
13.6.5 Estimation of Power Consumption
346(2)
13.7 Discussion
348(1)
13.8 Conclusion
349(1)
References
350(3)
14 Security and Privacy Issues and Solutions for Fog 353(22)
Mithun Mukherjee
Mohamed Amine Ferrag
Leandros Maglaras
Abdelouahid Derhab
Mohammad Aazam
14.1 Introduction
353(7)
14.1.1 Major Limitations in Traditional Cloud Computing
353(1)
14.1.2 Fog Computing: An Edge Computing Paradigm
354(3)
14.1.3 A Three-Tier Fog Computing Architecture
357(3)
14.2 Security and Privacy Challenges Posed by Fog Computing
360(1)
14.3 Existing Research on Security and Privacy Issues in Fog Computing
361(5)
14.3.1 Privacy-preserving
361(2)
14.3.2 Authentication
363(1)
14.3.3 Access Control
363(1)
14.3.4 Malicious attacks
364(2)
14.4 Open Questions and Research Challenges
366(3)
14.4.1 Trust
367(1)
14.4.2 Privacy preservation
367(1)
14.4.3 Authentication
367(1)
14.4.4 Malicious Attacks and Intrusion Detection
368(1)
14.4.5 Cross-border Issues and Fog Forensic
369(1)
14.5 Summary
369(1)
Exercises
370(1)
References
370(5)
Index 375
YANG YANG, PHD is a professor with ShanghaiTech University and a Co-Director of Shanghai Institute of Fog Computing Technology (SHIFT), China.



JIANWEI HUANG, PHD is a Presidential Chair Professor and the Associate Dean of School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, and the Associate Director of Shenzhen Institute of Artificial Intelligence and Robotics for Society, China.

TAO ZHANG, PHD is currently with the National Institute of Standards and Technology (NIST), USA.

JOE WEINMAN is the former Senior Vice President of Cloud Services and Strategy at Telx, and is the founder of Cloudonomics, which takes a rigorous, multidisciplinary approach to valuing the cloud. He is the Cloud economics and strategy editor for IEEE Cloud Computing magazine and author of Cloudonomics: The Business Value of Cloud Computing and Digital Disciplines: Attaining Market Leadership via the Cloud, Big Data, Social, Mobile, and the Internet of Things.