Table Of ContentIntegrated
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
