Integrated networking, caching, and computing

251 Pages·2018·6.906 MB·English

by  Huang, Tao, Liu, Yunjie, Yu, F. Richard

Integrated
Networking, Caching,
and Computing

Integrated
Networking, Caching,
and Computing
F. Richard Yu
Tao Huang
Yunjie Liu
CRC Press
Taylor & Francis Group
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Contents
1 Overview, Motivations and Frameworks . . . . . . . . . . . 1
1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.1 Recent advances in wireless networking . . . . . . . . 2
1.1.2 Caching . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.1.3 Computing . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 Motivations and requirements . . . . . . . . . . . . . . . . . 7
1.2.1 What is integration of networking, caching and
computing? . . . . . . . . . . . . . . . . . . . . . . . . 7
1.2.2 Whydoweneedintegrationofnetworking,cachingand
computing? . . . . . . . . . . . . . . . . . . . . . . . . 8
1.2.2.1 Thegrowthofnetworkingaloneisnotsustain-
able . . . . . . . . . . . . . . . . . . . . . . . 8
1.2.2.2 Thebenefitsbroughtbytheintegrationofnet-
working,caching and computing . . . . . . . 10
1.2.3 The requirements of integration of networking, caching
and computing . . . . . . . . . . . . . . . . . . . . . . 11
1.2.3.1 Coexistence . . . . . . . . . . . . . . . . . . . 11
1.2.3.2 Flexibility . . . . . . . . . . . . . . . . . . . 11
1.2.3.3 Manageability and programmability . . . . . 11
1.2.3.4 Heterogeneity . . . . . . . . . . . . . . . . . 12
1.2.3.5 Scalability . . . . . . . . . . . . . . . . . . . 12
1.2.3.6 Stability and convergence . . . . . . . . . . . 12
1.2.3.7 Mobility . . . . . . . . . . . . . . . . . . . . 12
1.2.3.8 Backwardcompatibility . . . . . . . . . . . . 12
1.3 Frameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.3.1 Caching-networking framework . . . . . . . . . . . . . 13
1.3.1.1 D2D delivery (Fig. 1.2a) . . . . . . . . . . . 14
1.3.1.2 Multihop delivery via D2D relay (Fig. 1.2b). 14
v
vi (cid:4) Contents
1.3.1.3 Cooperative D2D delivery (Fig. 1.2c) . . . . 14
1.3.1.4 Direct SBS delivery (Fig. 1.2d) . . . . . . . . 14
1.3.1.5 Cooperative SBS delivery (Fig. 1.2e). . . . . 14
1.3.2 Computing-networking framework . . . . . . . . . . . 18
1.3.2.1 Cloud mobile media . . . . . . . . . . . . . . 18
1.3.2.2 Mobile edge computing . . . . . . . . . . . . 19
1.3.3 Caching-computing framework . . . . . . . . . . . . . 19
1.3.4 Caching-computing-networkingframework . . . . . . . 22
1.3.4.1 Networking-caching-computingconvergence . 22
1.3.4.2 Networking and computing assisted caching . 23
1.3.5 A use case . . . . . . . . . . . . . . . . . . . . . . . . . 23
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2 Performance Metrics and Enabling Technologies . . . . . . 33
2.1 Performance metrics . . . . . . . . . . . . . . . . . . . . . . . 33
2.1.1 General metrics . . . . . . . . . . . . . . . . . . . . . . 33
2.1.1.1 Cost . . . . . . . . . . . . . . . . . . . . . . . 33
2.1.1.2 Revenue. . . . . . . . . . . . . . . . . . . . . 36
2.1.1.3 Recovery time . . . . . . . . . . . . . . . . . 36
2.1.2 Networking-relatedmetrics . . . . . . . . . . . . . . . 36
2.1.2.1 Coverageand capacity (throughput) . . . . . 36
2.1.2.2 Deployment efficiency . . . . . . . . . . . . . 36
2.1.2.3 Spectral efficiency . . . . . . . . . . . . . . . 37
2.1.2.4 Energy efficiency . . . . . . . . . . . . . . . . 37
2.1.2.5 QoS . . . . . . . . . . . . . . . . . . . . . . . 37
2.1.2.6 Signaling delay and service latency . . . . . . 37
2.1.3 Caching-related metrics . . . . . . . . . . . . . . . . . 38
2.1.3.1 Average latency . . . . . . . . . . . . . . . . 38
2.1.3.2 Hop-count . . . . . . . . . . . . . . . . . . . 38
2.1.3.3 Load fairness . . . . . . . . . . . . . . . . . . 38
2.1.3.4 Responses per request . . . . . . . . . . . . . 39
2.1.3.5 Cache hits . . . . . . . . . . . . . . . . . . . 39
2.1.3.6 Caching efficiency . . . . . . . . . . . . . . . 39
2.1.3.7 Caching frequency . . . . . . . . . . . . . . . 39
2.1.3.8 Cache diversity . . . . . . . . . . . . . . . . . 39
2.1.3.9 Cache redundancy . . . . . . . . . . . . . . . 39
2.1.3.10 Absorption time . . . . . . . . . . . . . . . . 39
2.1.4 Computing-related metrics . . . . . . . . . . . . . . . 39
2.1.4.1 Execution time . . . . . . . . . . . . . . . . . 39
2.1.4.2 Energy consumption . . . . . . . . . . . . . . 40
2.1.4.3 Computation dropping cost . . . . . . . . . . 40
2.1.4.4 Throughput . . . . . . . . . . . . . . . . . . 40
2.2 Enabling technologies . . . . . . . . . . . . . . . . . . . . . . 40
2.2.1 Caching-networking . . . . . . . . . . . . . . . . . . . 41
Contents (cid:4) vii
2.2.1.1 Caching in heterogeneous networks . . . . . 41
2.2.1.2 Caching in information-centric networking . 42
2.2.1.3 Caching in D2D networking. . . . . . . . . . 43
2.2.1.4 Others . . . . . . . . . . . . . . . . . . . . . 44
2.2.2 Computing-networking . . . . . . . . . . . . . . . . . . 44
2.2.2.1 Cloud computing and networking . . . . . . 44
2.2.2.2 Fog computing and networking . . . . . . . . 46
2.2.2.3 Mobile edge computing and networking . . . 47
2.2.3 Caching-computing-networking . . . . . . . . . . . . . 49
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3 Edge Caching with Wireless Software-Defined Networking 65
3.1 Wireless SDN and edge caching . . . . . . . . . . . . . . . . 66
3.1.1 Motivations and contributions. . . . . . . . . . . . . . 66
3.1.2 Literature review . . . . . . . . . . . . . . . . . . . . . 67
3.2 System model and problem formulation . . . . . . . . . . . . 68
3.2.1 Network Model . . . . . . . . . . . . . . . . . . . . . . 68
3.2.1.1 Wireless communication model . . . . . . . . 68
3.2.1.2 Proactive wireless edge caching model . . . . 71
3.2.1.3 Video QoE model . . . . . . . . . . . . . . . 72
3.2.2 Problem formulation . . . . . . . . . . . . . . . . . . . 73
3.3 Bandwidth provisioning and edge caching . . . . . . . . . . . 75
3.3.1 Proposed caching decoupling via dual decomposition . 76
3.3.2 Upper bound approach to solving (3.14) . . . . . . . . 77
3.3.3 Rounding methods based on marginal benefits . . . . 79
3.3.4 Computational complexity, convergence and optimality 80
3.3.5 Implementation design in SDWNs . . . . . . . . . . . 82
3.4 Simulation results and discussion . . . . . . . . . . . . . . . . 83
3.4.1 Algorithm performance . . . . . . . . . . . . . . . . . 84
3.4.2 Network performance . . . . . . . . . . . . . . . . . . 86
3.4.2.1 Delay . . . . . . . . . . . . . . . . . . . . . . 86
3.4.2.2 QoE guarantee . . . . . . . . . . . . . . . . . 88
3.4.3 Utilization. . . . . . . . . . . . . . . . . . . . . . . . . 88
3.4.3.1 Caching resources . . . . . . . . . . . . . . . 88
3.4.3.2 Backhaul resource . . . . . . . . . . . . . . . 89
3.5 Conclusions and future work . . . . . . . . . . . . . . . . . . 90
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4 Resource Allocation for 3C-Enabled HetNets . . . . . . . . 95
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
4.2 Architecture overview . . . . . . . . . . . . . . . . . . . . . . 98
4.2.1 Wireless network virtualization . . . . . . . . . . . . . 98
4.2.2 Information-centric networking . . . . . . . . . . . . . 98
4.2.3 Mobile edge computing . . . . . . . . . . . . . . . . . 99
viii (cid:4) Contents
4.2.4 3C-enabled virtualized HetNets . . . . . . . . . . . . . 99
4.3 Virtualized multi-resources allocation . . . . . . . . . . . . . 102
4.3.1 System model . . . . . . . . . . . . . . . . . . . . . . . 102
4.3.1.1 Virtual heterogeneous networks model . . . . 102
4.3.1.2 Computing model . . . . . . . . . . . . . . . 102
4.3.1.3 Caching model . . . . . . . . . . . . . . . . . 105
4.3.2 Problem formulation . . . . . . . . . . . . . . . . . . . 106
4.3.3 Problem reformulation . . . . . . . . . . . . . . . . . . 107
4.4 Resource allocation via ADMM . . . . . . . . . . . . . . . . 109
4.4.1 Decoupling of association indicators . . . . . . . . . . 109
4.4.2 Problem solving via ADMM . . . . . . . . . . . . . . . 110
4.4.3 Algorithm analysis: computational complexity. . . . . 113
4.5 Simulation results and discussion . . . . . . . . . . . . . . . . 113
4.5.1 Parameter settings . . . . . . . . . . . . . . . . . . . . 114
4.5.2 Alternative schemes . . . . . . . . . . . . . . . . . . . 115
4.5.3 Performance evaluation . . . . . . . . . . . . . . . . . 115
4.6 Conclusion and future work . . . . . . . . . . . . . . . . . . . 121
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
5 Network Slicing and Caching in 5G Cellular Networks . . 125
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
5.2 System model and problem formulation . . . . . . . . . . . . 128
5.2.1 Overviewofa5Gcorenetworkinvolvingnetworkslicing
and caching . . . . . . . . . . . . . . . . . . . . . . . 129
5.2.2 System model and problem formulation . . . . . . . . 130
5.3 Caching resource allocation based on the CRO algorithm . . 133
5.3.1 Brief introduction to the CRO algorithm . . . . . . . 134
5.3.2 Caching resource allocation based on the CRO
algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 134
5.3.3 Complexity analysis . . . . . . . . . . . . . . . . . . . 138
5.4 Simulation results and discussions . . . . . . . . . . . . . . . 139
5.5 Conclusions and future work . . . . . . . . . . . . . . . . . . 144
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
6 Joint optimization of 3C . . . . . . . . . . . . . . . . . . . . . 149
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
6.2 System model . . . . . . . . . . . . . . . . . . . . . . . . . . 151
6.2.1 Network model . . . . . . . . . . . . . . . . . . . . . . 151
6.2.2 Communication model . . . . . . . . . . . . . . . . . . 154
6.2.3 Computation model . . . . . . . . . . . . . . . . . . . 154
6.2.3.1 Local computing . . . . . . . . . . . . . . . . 155
6.2.3.2 MEC server computing . . . . . . . . . . . . 155
6.2.4 Caching model . . . . . . . . . . . . . . . . . . . . . . 156
6.2.5 Utility function . . . . . . . . . . . . . . . . . . . . . . 156
Contents (cid:4) ix
6.3 Problem formulation, transformation and decomposition . . 158
6.3.1 Problem formulation . . . . . . . . . . . . . . . . . . . 158
6.3.2 Problem transformation . . . . . . . . . . . . . . . . . 159
6.3.2.1 Binary variable relaxation. . . . . . . . . . . 160
6.3.2.2 Substitution of the product term . . . . . . . 160
6.3.3 Convexity . . . . . . . . . . . . . . . . . . . . . . . . . 161
6.3.4 Problem decomposition . . . . . . . . . . . . . . . . . 162
6.4 Problem solving via ADMM . . . . . . . . . . . . . . . . . . 164
6.4.1 Augmented Lagrangian and ADMM sequential itera-
tions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
6.4.2 Local variables update . . . . . . . . . . . . . . . . . . 166
6.4.3 Global variables and Lagrange multipliers update . . . 167
6.4.4 Algorithm stopping criterion and convergence . . . . . 169
6.4.5 Binary variables recovery . . . . . . . . . . . . . . . . 169
6.4.6 Feasibility, complexity and summary of the algorithm 170
6.5 Simulation results and discussion . . . . . . . . . . . . . . . . 172
6.6 Conclusions and future work . . . . . . . . . . . . . . . . . . 179
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
7 Software-Defined Networking, Caching and Computing . . 185
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 186
7.2 Recent advances in networking, caching and computing . . . 188
7.2.1 Software-defined networking . . . . . . . . . . . . . . . 188
7.2.2 Information centric networking . . . . . . . . . . . . . 189
7.2.3 Cloud and fog computing . . . . . . . . . . . . . . . . 189
7.2.4 An integrated framework for software-defined network-
ing, caching and computing . . . . . . . . . . . . . . . 190
7.2.4.1 Software-defined and information-centric
control . . . . . . . . . . . . . . . . . . . . . 190
7.2.4.2 Service-orientedrequest/reply paradigm . . . 190
7.2.4.3 In-network caching and computing . . . . . . 191
7.3 Architecture of the integrated framework SD-NCC . . . . . 191
7.3.1 The data plane . . . . . . . . . . . . . . . . . . . . . . 191
7.3.2 The control plane. . . . . . . . . . . . . . . . . . . . . 193
7.3.3 The management plane . . . . . . . . . . . . . . . . . 197
7.3.4 The workflow of SD-NCC . . . . . . . . . . . . . . . . 198
7.4 System model . . . . . . . . . . . . . . . . . . . . . . . . . . 200
7.4.1 Network model . . . . . . . . . . . . . . . . . . . . . . 200
7.4.2 Caching/computing model. . . . . . . . . . . . . . . . 200
7.4.3 Server selection model . . . . . . . . . . . . . . . . . . 201
7.4.4 Routing model . . . . . . . . . . . . . . . . . . . . . . 201
7.4.5 Energy model . . . . . . . . . . . . . . . . . . . . . . . 201
7.4.5.1 Caching energy . . . . . . . . . . . . . . . . . 201
7.4.5.2 Computing energy . . . . . . . . . . . . . . . 202