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Integrated Networking, Caching, and Computing [Kõva köide]

, (Carlton University, Ottawa, Ontario, Canada),
  • Formaat: Hardback, 240 pages, kõrgus x laius: 234x156 mm, kaal: 500 g, 40 Illustrations, black and white
  • Ilmumisaeg: 13-Jun-2018
  • Kirjastus: CRC Press
  • ISBN-10: 1138089036
  • ISBN-13: 9781138089037
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Integrated Networking, Caching, and ComputingSuurem pilt
  • Formaat: Hardback, 240 pages, kõrgus x laius: 234x156 mm, kaal: 500 g, 40 Illustrations, black and white
  • Ilmumisaeg: 13-Jun-2018
  • Kirjastus: CRC Press
  • ISBN-10: 1138089036
  • ISBN-13: 9781138089037
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Püsilink: https://www.kriso.ee/db/9781138089037.html
Märksõnad:

This book features the major research advances on integrated networking, caching, and computing. Information-centric networking-based caching is one of the promising techniques for future networks. The cloud computing paradigm has been widely adopted to enable convenient, on-demand network access to a shared pool of configurable computing resources. In addition, fog/edge computing is proposed to deploy computing resources closer to end devices. From the perspective of applications, network, cache and compute are underlying enabling resources. How to manage, control and optimize these resources can have significant impacts on application performance.

1 Overview, Motivations and Frameworks
1(32)
1.1 Overview
2(5)
1.1.1 Recent advances in wireless networking
2(2)
1.1.2 Caching
4(1)
1.1.3 Computing
5(2)
1.2 Motivations and requirements
7(5)
1.2.1 What is integration of networking, caching and computing?
7(1)
1.2.2 Why do we need integration of networking, caching and computing?
8(1)
1.2.2.1 The growth of networking alone is not sustainable
8(2)
1.2.2.2 The benefits brought by the integration of networking, caching and computing
10(1)
1.2.3 The requirements of integration of networking, caching and computing
11(1)
1.2.3.1 Coexistence
11(1)
1.2.3.2 Flexibility
11(1)
1.2.3.3 Manageability and programmability
11(1)
1.2.3.4 Heterogeneity
12(1)
1.2.3.5 Scalability
12(1)
1.2.3.6 Stability and convergence
12(1)
1.2.3.7 Mobility
12(1)
1.2.3.8 Backward compatibility
12(1)
1.3 Frameworks
12(21)
1.3.1 Caching-networking framework
13(1)
1.3.1.1 D2D delivery (Fig. 1.2a)
14(1)
1.3.1.2 Multihop delivery via D2D relay (Fig. 1.2b)
14(1)
1.3.1.3 Cooperative D2D delivery (Fig. 1.2c)
14(1)
1.3.1.4 Direct SBS delivery (Fig. 1.2d)
14(1)
1.3.1.5 Cooperative SBS delivery (Fig. 1.2e)
14(4)
1.3.2 Computing-networking framework
18(1)
1.3.2.1 Cloud mobile media
18(1)
1.3.2.2 Mobile edge computing
19(1)
1.3.3 Caching-computing framework
19(3)
1.3.4 Caching-computing-networking framework
22(1)
1.3.4.1 Networking-caching-computing convergence
22(1)
1.3.4.2 Networking and computing assisted caching
23(1)
1.3.5 A use case
23(2)
References
25(8)
2 Performance Metrics and Enabling Technologies
33(32)
2.1 Performance metrics
33(7)
2.1.1 General metrics
33(1)
2.1.1.1 Cost
33(3)
2.1.1.2 Revenue
36(1)
2.1.1.3 Recovery time
36(1)
2.1.2 Networking-related metrics
36(1)
2.1.2.1 Coverage and capacity (throughput)
36(1)
2.1.2.2 Deployment efficiency
36(1)
2.1.2.3 Spectral efficiency
37(1)
2.1.2.4 Energy efficiency
37(1)
2.1.2.5 QoS
37(1)
2.1.2.6 Signaling delay and service latency
37(1)
2.1.3 Caching-related metrics
38(1)
2.1.3.1 Average latency
38(1)
2.1.3.2 Hop-count
38(1)
2.1.3.3 Load fairness
38(1)
2.1.3.4 Responses per request
39(1)
2.1.3.5 Cache hits
39(1)
2.1.3.6 Caching efficiency
39(1)
2.1.3.7 Caching frequency
39(1)
2.1.3.8 Cache diversity
39(1)
2.1.3.9 Cache redundancy
39(1)
2.1.3.10 Absorption time
39(1)
2.1.4 Computing-related metrics
39(1)
2.1.4.1 Execution time
39(1)
2.1.4.2 Energy consumption
40(1)
2.1.4.3 Computation dropping cost
40(1)
2.1.4.4 Throughput
40(1)
2.2 Enabling technologies
40(25)
2.2.1 Caching-networking
41(1)
2.2.1.1 Caching in heterogeneous networks
41(1)
2.2.1.2 Caching in information-centric networking
42(1)
2.2.1.3 Caching in D2D networking
43(1)
2.2.1.4 Others
44(1)
2.2.2 Computing-networking
44(1)
2.2.2.1 Cloud computing and networking
44(2)
2.2.2.2 Fog computing and networking
46(1)
2.2.2.3 Mobile edge computing and networking
47(2)
2.2.3 Caching-computing-networking
49(9)
References
58(7)
3 Edge Caching with Wireless Software-Defined Networking
65(30)
3.1 Wireless SDN and edge caching
66(2)
3.1.1 Motivations and contributions
66(1)
3.1.2 Literature review
67(1)
3.2 System model and problem formulation
68(7)
3.2.1 Network Model
68(1)
3.2.1.1 Wireless communication model
68(3)
3.2.1.2 Proactive wireless edge caching model
71(1)
3.2.1.3 Video QoE model
72(1)
3.2.2 Problem formulation
73(2)
3.3 Bandwidth provisioning and edge caching
75(8)
3.3.1 Proposed caching decoupling via dual decomposition
76(1)
3.3.2 Upper bound approach to solving (3.14)
77(2)
3.3.3 Rounding methods based on marginal benefits
79(1)
3.3.4 Computational complexity, convergence and optimality
80(2)
3.3.5 Implementation design in SDWNs
82(1)
3.4 Simulation results and discussion
83(7)
3.4.1 Algorithm performance
84(2)
3.4.2 Network performance
86(1)
3.4.2.1 Delay
86(2)
3.4.2.2 QoE guarantee
88(1)
3.4.3 Utilization
88(1)
3.4.3.1 Caching resources
88(1)
3.4.3.2 Backhaul resource
89(1)
3.5 Conclusions and future work
90(5)
References
90(5)
4 Resource Allocation for 3C-Enabled HetNets
95(30)
4.1 Introduction
96(2)
4.2 Architecture overview
98(4)
4.2.1 Wireless network virtualization
98(1)
4.2.2 Information-centric networking
98(1)
4.2.3 Mobile edge computing
99(1)
4.2.4 3C-enabled virtualized HetNets
99(3)
4.3 Virtualized multi-resources allocation
102(7)
4.3.1 System model
102(1)
4.3.1.1 Virtual heterogeneous networks model
102(1)
4.3.1.2 Computing model
102(3)
4.3.1.3 Caching model
105(1)
4.3.2 Problem formulation
106(1)
4.3.3 Problem reformulation
107(2)
4.4 Resource allocation via ADMM
109(4)
4.4.1 Decoupling of association indicators
109(1)
4.4.2 Problem solving via ADMM
110(3)
4.4.3 Algorithm analysis: computational complexity
113(1)
4.5 Simulation results and discussion
113(8)
4.5.1 Parameter settings
114(1)
4.5.2 Alternative schemes
115(1)
4.5.3 Performance evaluation
115(6)
4.6 Conclusion and future work
121(4)
References
122(3)
5 Network Slicing and Caching in 5G Cellular Networks
125(24)
5.1 Introduction
126(2)
5.2 System model and problem formulation
128(5)
5.2.1 Overview of a 5G core network involving network slicing and caching
129(1)
5.2.2 System model and problem formulation
130(3)
5.3 Caching resource allocation based on the CRO algorithm
133(6)
5.3.1 Brief introduction to the CRO algorithm
134(1)
5.3.2 Caching resource allocation based on the CRO algorithm
134(4)
5.3.3 Complexity analysis
138(1)
5.4 Simulation results and discussions
139(5)
5.5 Conclusions and future work
144(5)
References
144(5)
6 Joint optimization of 3C
149(36)
6.1 Introduction
149(2)
6.2 System model
151(7)
6.2.1 Network model
151(3)
6.2.2 Communication model
154(1)
6.2.3 Computation model
154(1)
6.2.3.1 Local computing
155(1)
6.2.3.2 MEC server computing
155(1)
6.2.4 Caching model
156(1)
6.2.5 Utility function
156(2)
6.3 Problem formulation, transformation and decomposition
158(6)
6.3.1 Problem formulation
158(1)
6.3.2 Problem transformation
159(1)
6.3.2.1 Binary variable relaxation
160(1)
6.3.2.2 Substitution of the product term
160(1)
6.3.3 Convexity
161(1)
6.3.4 Problem decomposition
162(2)
6.4 Problem solving via ADMM
164(8)
6.4.1 Augmented Lagrangian and ADMM sequential iterations
164(2)
6.4.2 Local variables update
166(1)
6.4.3 Global variables and Lagrange multipliers update
167(2)
6.4.4 Algorithm stopping criterion and convergence
169(1)
6.4.5 Binary variables recovery
169(1)
6.4.6 Feasibility, complexity and summary of the algorithm
170(2)
6.5 Simulation results and discussion
172(7)
6.6 Conclusions and future work
179(6)
References
179(6)
7 Software-Defined Networking, Caching and Computing
185(30)
7.1 Introduction
186(2)
7.2 Recent advances in networking, caching and computing
188(3)
7.2.1 Software-defined networking
188(1)
7.2.2 Information centric networking
189(1)
7.2.3 Cloud and fog computing
189(1)
7.2.4 An integrated framework for software-defined networking, caching and computing
190(1)
7.2.4.1 Software-defined and information-centric control
190(1)
7.2.4.2 Service-oriented request/reply paradigm
190(1)
7.2.4.3 In-network caching and computing
191(1)
7.3 Architecture of the integrated framework SD-NCC
191(9)
7.3.1 The data plane
191(2)
7.3.2 The control plane
193(4)
7.3.3 The management plane
197(1)
7.3.4 The workflow of SD-NCC
198(2)
7.4 System model
200(2)
7.4.1 Network model
200(1)
7.4.2 Caching/computing model
200(1)
7.4.3 Server selection model
201(1)
7.4.4 Routing model
201(1)
7.4.5 Energy model
201(1)
7.4.5.1 Caching energy
201(1)
7.4.5.2 Computing energy
202(1)
7.4.5.3 Transmission energy
202(1)
7.5 Caching/computing/bandwidth resource allocation
202(4)
7.5.1 Problem formulation
203(1)
7.5.1.1 Objective function
203(1)
7.5.1.2 Formulation
203(1)
7.5.2 Caching/computing capacity allocation
204(1)
7.5.3 The exhaustive-search algorithm
205(1)
7.6 Simulation results and discussion
206(3)
7.6.1 Network usage cost
206(1)
7.6.2 Energy consumption cost
207(1)
7.6.3 Optimal deployment numbers
208(1)
7.7 Open research issues
209(2)
7.7.1 Scalable SD-NCC controller design
209(1)
7.7.2 Local autonomy in the SD-NCC data plane
210(1)
7.7.3 Networking/caching/computing resource allocation strategies
210(1)
7.8 Conclusions
211(4)
References
211(4)
8 Challenges and Broader Perspectives
215(12)
8.1 Challenges
215(4)
8.1.1 Stringent latency requirements
215(1)
8.1.2 Tremendous amount of data against network bandwidth constraints
216(1)
8.1.3 Uninterruptable services against intermittent connectivity
216(1)
8.1.4 Interference of multiple interfaces
216(1)
8.1.5 Network effectiveness in the face of mobility
217(1)
8.1.6 The networking-caching-computing capacity
218(1)
8.1.7 The networking-caching-computing tradeoffs
218(1)
8.1.8 Security
218(1)
8.1.9 Convergence and consistency
219(1)
8.1.10 End-to-end architectural tradeoffs
219(1)
8.2 Broader perspectives
219(8)
8.2.1 Software-defined networking
219(1)
8.2.2 Network function virtualization
220(1)
8.2.3 Wireless network virtualization
221(1)
8.2.4 Big data analytics
221(2)
8.2.5 Deep reinforcement learning
223(1)
References
223(4)
Index 227
F. Richard Yu received the PhD degree in electrical engineering from the University of British Columbia (UBC) in 2003. From 2002 to 2006, he was with Ericsson (in Lund, Sweden) and a start-up in California, USA. He joined Carleton University in 2007, where he is currently a Professor. He received the IEEE Outstanding Service Award in 2016, IEEE Outstanding Leadership Award in 2013, Carleton Research Achievement Award in 2012, the Ontario Early Researcher Award (formerly Premiers Research Excellence Award) in 2011, the Excellent Contribution Award at IEEE/IFIP TrustCom 2010, the Leadership Opportunity Fund Award from Canada Foundation of Innovation in 2009 and the Best Paper Awards at IEEE ICC 2014, Globecom 2012, IEEE/IFIP TrustCom 2009 and Int'l Conference on Networking 2005. His research interests include cross-layer/cross-system design, security, green ICT and QoS provisioning in wireless-based systems.Dr. Yu serves on the editorial boards of several journals, including Co-Editor-in-Chief for Ad Hoc & Sensor Wireless Networks, Lead Series Editor for IEEE Transactions on Vehicular Technology, and IEEE Transactions on Green Communications and Networking, IEEE Communications Surveys & Tutorials. He has served as the Technical Program Committee (TPC) Co-Chair of numerous conferences. Dr. Yu is a registered Professional Engineer in the province of Ontario, Canada, and a senior member of the IEEE. He serves as a member of Board of Governors of the IEEE Vehicular Technology Society. Tao Huang received his B.S degree in communication engineering from Nankai University, Tianjin, China, in 2002, the M.S. and Ph.D. degree in communication and information system from Beijing University of Posts and Telecommunications, Beijing, China, in 2004 and 2007 respectively. He is currently an associate professor at Beijing University of Posts and Telecommunications. His current research interests include network architecture and network virtualization. Yunjie Liu received his B.S degree in technical physics from Peking University, beijing, China, in 1968. He is currently an academician of China Academy of Engineering, the chief of the science and technology committee of China Unicom, and the dean of the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications. His current research interests include next generation networks, network architecture and management.