Integrated networking, caching, and computing

Integrated Networking, Caching, and Computing Integrated Networking, Caching, and Computing F Richard Yu Tao Huang Yunjie Liu CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2018 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S Government works Printed on acid-free paper Version Date: 20180428 International Standard Book Number-13: 978-1-138-08903-7 (Hardback) This book contains information obtained from authentic and highly regarded sources Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained If any 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trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com Contents Overview, Motivations and Frameworks 1.1 1.2 1.3 Overview 1.1.1 Recent advances in wireless networking 1.1.2 Caching 1.1.3 Computing Motivations and requirements 1.2.1 What is integration of networking, caching and computing? 1.2.2 Why we need integration of networking, caching and computing? 1.2.2.1 The growth of networking alone is not sustainable 1.2.2.2 The benefits brought by the integration of networking, caching and computing 1.2.3 The requirements of integration of networking, caching and computing 1.2.3.1 Coexistence 1.2.3.2 Flexibility 1.2.3.3 Manageability and programmability 1.2.3.4 Heterogeneity 1.2.3.5 Scalability 1.2.3.6 Stability and convergence 1.2.3.7 Mobility 1.2.3.8 Backward compatibility Frameworks 1.3.1 Caching-networking framework 1.3.1.1 D2D delivery (Fig 1.2a) 1.3.1.2 Multihop delivery via D2D relay (Fig 1.2b) 2 7 8 10 11 11 11 11 12 12 12 12 12 12 13 14 14 v Contents vi 1.3.1.3 Cooperative D2D delivery (Fig 1.2c) 1.3.1.4 Direct SBS delivery (Fig 1.2d) 1.3.1.5 Cooperative SBS delivery (Fig 1.2e) 1.3.2 Computing-networking framework 1.3.2.1 Cloud mobile media 1.3.2.2 Mobile edge computing 1.3.3 Caching-computing framework 1.3.4 Caching-computing-networking framework 1.3.4.1 Networking-caching-computing convergence 1.3.4.2 Networking and computing assisted caching 1.3.5 A use case References 14 14 14 18 18 19 19 22 22 23 23 25 Performance Metrics and Enabling Technologies 33 2.1 2.2 Performance metrics 2.1.1 General metrics 2.1.1.1 Cost 2.1.1.2 Revenue 2.1.1.3 Recovery time 2.1.2 Networking-related metrics 2.1.2.1 Coverage and capacity (throughput) 2.1.2.2 Deployment efficiency 2.1.2.3 Spectral efficiency 2.1.2.4 Energy efficiency 2.1.2.5 QoS 2.1.2.6 Signaling delay and service latency 2.1.3 Caching-related metrics 2.1.3.1 Average latency 2.1.3.2 Hop-count 2.1.3.3 Load fairness 2.1.3.4 Responses per request 2.1.3.5 Cache hits 2.1.3.6 Caching efficiency 2.1.3.7 Caching frequency 2.1.3.8 Cache diversity 2.1.3.9 Cache redundancy 2.1.3.10 Absorption time 2.1.4 Computing-related metrics 2.1.4.1 Execution time 2.1.4.2 Energy consumption 2.1.4.3 Computation dropping cost 2.1.4.4 Throughput Enabling technologies 2.2.1 Caching-networking 33 33 33 36 36 36 36 36 37 37 37 37 38 38 38 38 39 39 39 39 39 39 39 39 39 40 40 40 40 41 Contents 2.2.1.1 Caching in heterogeneous networks 2.2.1.2 Caching in information-centric networking 2.2.1.3 Caching in D2D networking 2.2.1.4 Others 2.2.2 Computing-networking 2.2.2.1 Cloud computing and networking 2.2.2.2 Fog computing and networking 2.2.2.3 Mobile edge computing and networking 2.2.3 Caching-computing-networking References vii 41 42 43 44 44 44 46 47 49 58 Edge Caching with Wireless Software-Defined Networking 65 3.1 Wireless SDN and edge caching 3.1.1 Motivations and contributions 3.1.2 Literature review 3.2 System model and problem formulation 3.2.1 Network Model 3.2.1.1 Wireless communication model 3.2.1.2 Proactive wireless edge caching model 3.2.1.3 Video QoE model 3.2.2 Problem formulation 3.3 Bandwidth provisioning and edge caching 3.3.1 Proposed caching decoupling via dual decomposition 3.3.2 Upper bound approach to solving (3.14) 3.3.3 Rounding methods based on marginal benefits 3.3.4 Computational complexity, convergence and optimality 3.3.5 Implementation design in SDWNs 3.4 Simulation results and discussion 3.4.1 Algorithm performance 3.4.2 Network performance 3.4.2.1 Delay 3.4.2.2 QoE guarantee 3.4.3 Utilization 3.4.3.1 Caching resources 3.4.3.2 Backhaul resource 3.5 Conclusions and future work References 66 66 67 68 68 68 71 72 73 75 76 77 79 80 82 83 84 86 86 88 88 88 89 90 90 Resource Allocation for 3C-Enabled HetNets 95 4.1 4.2 Introduction Architecture overview 4.2.1 Wireless network virtualization 4.2.2 Information-centric networking 4.2.3 Mobile edge computing 96 98 98 98 99 Contents viii 4.2.4 3C-enabled virtualized HetNets Virtualized multi-resources allocation 4.3.1 System model 4.3.1.1 Virtual heterogeneous networks model 4.3.1.2 Computing model 4.3.1.3 Caching model 4.3.2 Problem formulation 4.3.3 Problem reformulation 4.4 Resource allocation via ADMM 4.4.1 Decoupling of association indicators 4.4.2 Problem solving via ADMM 4.4.3 Algorithm analysis: computational complexity 4.5 Simulation results and discussion 4.5.1 Parameter settings 4.5.2 Alternative schemes 4.5.3 Performance evaluation 4.6 Conclusion and future work References 99 102 102 102 102 105 106 107 109 109 110 113 113 114 115 115 121 122 Network Slicing and Caching in 5G Cellular Networks 125 4.3 5.1 5.2 Introduction System model and problem formulation 5.2.1 Overview of a 5G core network involving network slicing and caching 5.2.2 System model and problem formulation 5.3 Caching resource allocation based on the CRO algorithm 5.3.1 Brief introduction to the CRO algorithm 5.3.2 Caching resource allocation based on the CRO algorithm 5.3.3 Complexity analysis 5.4 Simulation results and discussions 5.5 Conclusions and future work References 126 128 Joint optimization of 3C 149 6.1 6.2 Introduction System model 6.2.1 Network model 6.2.2 Communication model 6.2.3 Computation model 6.2.3.1 Local computing 6.2.3.2 MEC server computing 6.2.4 Caching model 6.2.5 Utility function 129 130 133 134 134 138 139 144 144 149 151 151 154 154 155 155 156 156 Contents 6.3 ix Problem formulation, transformation and decomposition 6.3.1 Problem formulation 6.3.2 Problem transformation 6.3.2.1 Binary variable relaxation 6.3.2.2 Substitution of the product term 6.3.3 Convexity 6.3.4 Problem decomposition 6.4 Problem solving via ADMM 6.4.1 Augmented Lagrangian and ADMM sequential iterations 6.4.2 Local variables update 6.4.3 Global variables and Lagrange multipliers update 6.4.4 Algorithm stopping criterion and convergence 6.4.5 Binary variables recovery 6.4.6 Feasibility, complexity and summary of the algorithm 6.5 Simulation results and discussion 6.6 Conclusions and future work References 164 166 167 169 169 170 172 179 179 Software-Defined Networking, Caching and Computing 185 7.1 7.2 7.3 7.4 Introduction Recent advances in networking, caching and computing 7.2.1 Software-defined networking 7.2.2 Information centric networking 7.2.3 Cloud and fog computing 7.2.4 An integrated framework for software-defined networking, caching and computing 7.2.4.1 Software-defined and information-centric control 7.2.4.2 Service-oriented request/reply paradigm 7.2.4.3 In-network caching and computing Architecture of the integrated framework SD-NCC 7.3.1 The data plane 7.3.2 The control plane 7.3.3 The management plane 7.3.4 The workflow of SD-NCC System model 7.4.1 Network model 7.4.2 Caching/computing model 7.4.3 Server selection model 7.4.4 Routing model 7.4.5 Energy model 7.4.5.1 Caching energy 7.4.5.2 Computing energy 158 158 159 160 160 161 162 164 186 188 188 189 189 190 190 190 191 191 191 193 197 198 200 200 200 201 201 201 201 202 226 References [30] Y He, Z Zhang, F R Yu, N Zhao, H Yin, V C M Leung, and Y Zhang, “Deep reinforcement learning-based optimization for cacheenabled opportunistic interference alignment wireless networks,” IEEE Trans Veh Tech., 2017, online [31] Y He, N Zhao, and H Yin, “Integrated networking, caching and computing for connected vehicles: A deep reinforcement learning approach,” IEEE Trans Veh Tech., vol 67, no 1, pp 44-55, Jan 2018 Index Page numbers followed by f indicate figure Page numbers followed by t indicate table A Absorption time metric, 35t, 39 Access point (AP), 23 Additive white Gaussian noise (AWGN), 104 ADMM See Alternating direction method of multipliers (ADMM) Algorithm performance, 84–85 Alternating direction method of multipliers (ADMM), 50, 97 problem solving via, 164–172 resource allocation via, 109–113 Amazon, 8, 197 Amazon’s Cloud Drive, revenue of, 11 AP See Access point (AP) API See Application programming interface (API) Apple, Apple’s iCloud, revenue of, 11 Application programming interface (API), 3, 188 AR See Augmented reality (AR) ARCchart report, Augmented Lagrangian, 110, 112, 164–166 Augmented reality (AR), 1, 5, 24 Average latency metric, 34t, 38 AWGN See Additive white Gaussian noise (AWGN) B Backhaul load with different network setups, 90f of video streaming, 89–90 Backhaul resource, 89–90 Backhaul usage vs number of small cells, 175f Backward and compatibility, in networking, coaching, and computing integration, 12 Bandwidth, 36 Bandwidth provisioning and edge caching, 75–82 Baseband unit (BBU), 3, 17, 20 Baseline (no cache), 84 Baseline (no SDWN), 84 Base station (BS), 3, Base station system application part (BSSAP), 222 BBU See Baseband unit (BBU) Beamforming (BF), 227 228 Index server selection (CCS-SS), 202 Caching decoupling via dual decomposition, proposed, 76–77 Caching efficiency metric, 35t, 39 Caching energy, in SD-NCC, 201 Caching frequency metric, 35t, 39 Caching model, 3C joint optimization, 156 Caching-networking, 41–44, 51t, 54t caching in D2D networking, 43 C caching in heterogeneous CaaS See Caching as a service networks, 41–42 (CaaS) caching in information-centric Cache A Replica On Modification networking, 42–43 (CAROM), 50 Caching-networking framework Cache diversity metric, 35t, 39 five delivery approaches, 13f, Cache-enabled SDWN, network 14–17 architecture of, 70f information-centric networking, Cache-hit probability, 39 16f Cache-hit rate, 39 signaling interaction procedures Cache-hit ratio, 39 for, 15f Cache hits metric, 35t, 39 Caching-related metrics, 34–35t Cache redundancy metric, 35t, 39 absorption time, 35t, 39 Caching, 4–5, See also 5G cellular average latency, 34t, 38 network slicing and caching; cache diversity, 35t, 39 Networking, caching, and cache hits, 35t, 39 computing integrated cache redundancy, 35t, 39 system caching efficiency, 35t, 39 in 5G networks, 126–127 caching frequency, 35t, 39 in D2D networking, 43 hop-count, 34t, 38 in heterogeneous networks, load fairness, 34t, 38 41–42 responses per request, 35t, 39 in information-centric Caching resources, 88–89 networking, 42–43 Capacity allocation, at mobile networks, 67 caching/computing, Caching as a service (CaaS), 21 204–205 Caching-computing framework, Capacity metric, 34t, 36 19–22, 20f CAPital EXpenditure (CapEx), 3, Caching/computing model, of 33, 36, 220, 221 SD-NCC, 200 CAROM See Cache A Replica On Caching-computing-networking, Modification (CAROM) 49–50, 53t, 56t CCMN See Cloud content-centric Caching/computing strategy and mobile networking (CCMN) BF See Beamforming (BF) Big data analytics, 221–222 Big data platform, requirements of, 20–21 Big signaling data, 221–222 Binary variable relaxation, 160 Binary variables, recovering, 169–170 BS See Base station (BS) BSSAP See Base station system application part (BSSAP) Index CCN See Content-centric networking (CCN) CCS-SS See Caching/computing strategy and server selection (CCS-SS) CDF See Cumulative distribution function (CDF) CDNs See Content Delivery Networks (CDNs) Centralized Scheme with (w.) Caching, 115 Channel state information (CSI), 97 Chemical Reaction Optimization (CRO), 125, 128, 133–139 Chunk, Cisco, 6, Cloud computing, 186, 189–190 Cloud computing and networking, 44–46 Cloud content-centric mobile networking (CCMN), 21 Cloudlet-based mobile cloud computing, Cloud mobile media (CMM), 8, 18–19, 18f Cloud Radio Access Network (C-RAN), 3, 21, 22 CMM See Cloud mobile media (CMM) CN See Core networks (CN) Coaching time See Absorption time metric Coexistence, in networking, coaching, and computing integration, 11 Communication in CCN, 189 device-to-device (D2D), role in wireless networks, in SD-NCC, 190 wireless, spectral efficiency of, Communication model, 3C joint optimization, 154 229 Computational complexity, 80–82, 113 Computation dropping cost metric, 35t, 40 Computation model, C joint optimization, 154–158 Computation offloading, 150–151 Computation resource allocation among UE, 174f Computing, 5–7, See also Networking, caching, and computing integrated system Computing energy, in SD-NCC, 202 Computing-networking, 52–53t, 55–56t cloud computing and networking, 44–46 fog computing and networking, 46–47 framework, 18–19 mobile edge computing and networking, 47–49 Computing-related metrics computation dropping cost, 35t, 40 energy consumption, 35t, 40 execution time, 35t, 39–40 throughput, 35t, 40 Consistency, convergence and, 219 Consumer cloud mobility services, revenues of, 11 Content-centric networking (CCN), 17, 126, 189 Content Delivery Networks (CDNs), 5, 16, 21 Content REQ (content request), 15 ContentServe, 14 Content Store (CS), 189 Control messaging, 221 Control plane in integrated framework SD-NCC, 193–196–197 SDN and, Convergence, 22, 80–82, 84f, 169, 219 230 Index Convexity of problem, 161–162 Cooperative D2D delivery, 13f, 14, 15 Cooperative multiple-input multiple-output (Coop-MIMO), 67 Cooperative SBS delivery, 13f, 14, 15 Coop-MIMO See Cooperative multiple-input multiple-output (Coop-MIMO) Core networks (CN), 18 Cost metric, 33 Coverage metric, 34t, 36 C-RAN See Cloud Radio Access Network (C-RAN) CRO See Chemical Reaction Optimization (CRO) CS See Content Store (CS) CSI See Channel state information (CSI) Cumulative distribution function (CDF), 86, 86f D D2D See Device-to-device (D2D) D2D communications See Device-to-device (D2D) communications D2D delivery, 13f, 14 D2D link-scheduling problem, 43 D2D networking, caching in, 43 Data against network bandwidth, in SDNCC, 216 Data delivery pattern, 10 Data packets, 189 Data plane local autonomy in SD-NCC, 210 SDN and, in SD-NCC, 191–193 Deep packet inspection (DPI), 191 Deep reinforcement learning, 223 Degree of computing (DoC), Delay download time, 38 Density of successful receptions (DSR), 43 Deployment efficiency (DE) metric, 34t, 36–37 Device-to-device (D2D), 13, 17 communications, fogging, 46 Direct data transmissions, Direct SBS delivery, 13f, 14, 15 Direct server-client link, 10 Distributed online social networks (DOSN), 17 Distributed Scheme without (w.o) Caching, 115 DNSs See Domain Name Servers (DNSs) DoC See Degree of computing (DoC) Domain Name Servers (DNSs), DOSN See Distributed online social networks (DOSN) DPI See Deep packet inspection (DPI) Dropbox, DSR See Density of successful receptions (DSR) Dual feasibility condition, 112, 169 Dynamic caching scheme, 84 E EC2 See Elastic Compute Cloud (EC2) Edge caching with wireless software-defined networking bandwidth provisioning and edge caching, 75–82 simulation results and discussion, 83–90 system model and problem formulation, 68–75 wireless SDN and edge caching, 66–68 Edging caching approach, 17 EE See Energy efficiency (EE) Elastic Compute Cloud (EC2), 46 Index Enabling technologies, 40–57 advantages and shortcomings of, 54–57t approaches specifying network architectures, 44 caching in D2D networking, 43 caching in heterogeneous networks, 41 caching in information-centric networking, 42–43 caching-networking, 41 for networking, caching, and computing integrated system, 51–53t eNB, 19 End-to-end architectural tradeoffs, 219 Energy consumption cost, 141–144, 207–208, 208f cost regarding content caching, 131 cost regarding content response, 131–132 metric, 35t, 40 Energy model, of SD-NCC, 201–202 EPC See Evolved packet core (EPC) European 5G project METIS, Evolved packet core (EPC), 17 Execution latency See Execution time metric Execution time metric, 35t, 39–40 F Facebook, 197 Face recognition, 1, Favorable propagation, Feasibility condition, 112, 169 Feasibility tolerances, 169 FemtoCaching, 17 FIB See Forwarding Information Base (FIB) 5G cellular network slicing and caching 231 caching resource allocation based on CRO algorithm, 133–139 conclusions and future work, 144 introduction to, 126–128, 127f simulation results and discussions, 139–144, 140f, 141f, 142f, 143f system model and problem formulation, 128–133, 133t 5G generation cellular communication systems, 41 networking technologies, 3, 16, 17 First dual residual, 112 Flexibility, in networking, coaching, and computing integration, 11 Fog computing, 6, 186, 189–190 benefits of, 150 and networking, 46–47 Fog RAN (F-RAN), 21–22 Forwarding Information Base (FIB), 189, 193 F-RAN See Fog RAN (F-RAN) G Gate-Server, 14, 15 General metrics, of the integrated system, 33–36, 34t cost metric, 33, 34t recovery time metric, 34t, 36 revenue, 34t, 36 Global consensus problem, 164 Global video traffic, 96 Google, 8, 197 H Hadoop platform, 222 Heterogeneity, in networking, coaching, and computing integration, 12 Heterogeneous network (HetNet), 3, 66, 68 232 Index resource allocation for 3-C enabled, 95–122 virtual, 102–109 HetNet See Heterogeneous network (HetNet) High computational capability, wireless cellular networks and, 149 High data rate, wireless cellular networks and, 149 Hop-count metric, 34t, 38 Hop-count rate, 38 Hop-count ratio, 38 Hop-reduction ratio, 38 I IC-CRNs See Information-centric cognitive radio networks (IC-CRNs) ICN See Information-centric networking (ICN); Information-centric wireless network (ICN) ILP See Integer linear programming (ILP) IMT-2020 See International Mobile Telecommunications for 2020 (IMT-2020) Information-centric cognitive radio networks (IC-CRNs), 42 Information-centric HetNets virtualization, with-in-network caching and MEC functions, 99, 101f Information-centric networking (ICN), 4, 5, 23 caching in, 42–43 for network slicing in 5G, 126–127 in resource allocation for 3C-enabled HetNets, 96, 98, 99 software-defined and, 190 technology, 186, 189 virtualization, 97 visualization framework, 16, 16f Information-centric paradigm, 10 Infrastructure provider (InP), 97, 98, 139–141 In-network caching, 65, 66, 96, 186, 196 In-network caching and computing, 191 In-network caching function MEC system and, 150 In-network caching mechanism, 10 In-network video caching, 68, 126 InP See Infrastructure provider (InP) Integer linear programming (ILP), 125 Integration, motivations of, 7–12 Interest packet, 189, 193 Interference of multiple interfaces, in SDNCC, 216–217 International Mobile Telecommunications for 2020 (IMT-2020), International Telecommunication Union (ITU), Internet of Things (IoT), 215 Internet protocols, 4–5 IoT See Internet of Things (IoT) IoT Hub, 47 ITU See International Telecommunication Union (ITU) J Joint allocation algorithm, proposed, 81 Juniper Research, 11 K Karush-Kuhn-Tucker (KKT) conditions, 78 Kinetic energy (KE), 134 Index L Lagrangian function, 76 Lagrangian relaxation, 45, 49 Langrange multipliers update, global variables and, 167–168 Latency requirements, of SDNCC, 215–216 Load fairness metric, 34t, 38 Local computing approach, 155 Local variable updating, primal-dual interior point for, 166–167 Logical network slices, Long term evolution (LTE), 68 Lyapunov optimization-based dynamic computation, 48 M Macro cell base station (MBS), 13 Macro eNodeB (MeNB), 151, 152 Manageability and programmability, in networking, coaching, and computing integration, 11 Management plane, in integrated framework SD-NCC, 197–198 Marginal benefits, rounding methods based on, 79–80 Markov decision process, 42–43 MATLAB 7.9.0 (R2009b), 114 MATLAB-based simulator, 172 MATLAB-based system level simulator, 83 MBS See Macro cell base station (MBS) MCC See Mobile cloud computing (MCC) MEC See Mobile edge computing (MEC) MEC server computing approach, 155–156 MEC system operator (MSO), 152 revenue vs available bandwidth, 177f 233 revenue vs MEC server computational capability, 175f revenue vs number of small cells, 176f Media Cloud, 49 MeNB See Macro eNodeB (MeNB) M-FMC) See Mobile Follow-me Cloud (M-FMC) Microwave (uW)-millimeter wave (MMV) networks, 217 Microwave power transfer (MPT), 45 Millimeter-wave (mmWave), MIMO See Multiple-input multiple-output (MIMO) MIMO networks See Multicell multiple-input multiple-output (MIMO) networks Mix integer nonlinear programming (MINLP), 75, 202 M/M/1 queueing model, 46 mmWave See Millimeter-wave (mmWave) MNO See Mobile network operator (MNO) Mobile applications, Mobile cloud computing (MCC), 6, 10 long-latency problem of, networking, 44–46 systems, 150 Mobile cloud storage, Mobile edge computing (MEC), 4, 7, 186 deployment, 19, 19f incorporating into mobile communication systems, 10 integrated into the BS, 99, 100f and networking, 47–49 in resource allocation for 3C-enabled HetNets, 96, 97, 99 wireless cellular networks and, 150 234 Index Mobile Follow-me Cloud (M-FMC), Networking, caching, and computing 217 integrated system See also Mobile network operator (MNO), 152 Software defined Mobile video mean-opinion score networking, caching, and (MV-MOS), 72 computing Mobile virtual network operator big data analytics, 221–222 (MVNO), 97, 102 caching, computing-networking Mobility, in networking, coaching, framework, 22–23 and computing integration, caching computing framework, 12 19–22 Mobility, managing, 217 caching-networking framework, Monte Carlo method, 83, 114, 172 13–17 Motivations of integration, 7–12 challenges, 215–219 MPT See Microwave power transfer computing networking (MPT) framework, 18–19 MSO See MEC system operator deep reinforcement learning, 223 (MSO) description of, 7–8 Multicast mechanisms, 10 enabling technologies, 40–50, Multihop delivery via D2D relay, 13f, 51–57t 14, 15 functionalities of, 1–2 Multiple-input multiple-output need for, 8–11 (MIMO), 3, 14, 218 network function virtualization, Multiple-input multiple-output 219–220 (MIMO) networks, 44 performance metrics, 33–40, MV-MOS See Mobile video 34–35t mean-opinion score requirements of, 11–12 (MV-MOS) schematic graph of, 9f MVNO See Mobile virtual network software defined networking, 219 operator (MVNO) use case, 23–25, 24f wireless network virtualization, N 221 Named data networking (NDN), 217 Networking and computing assisted Nano data centers (nDCs), 47 caching, 23 Nash equilibrium, 48 Networking-caching-computing Natural language processing, 1, capacity, 218 nDCs See Nano data centers Networking-caching-computing (nDCs) convergence, 22 NDN See Named data networking Networking-caching-computing (NDN) tradeoffs, 218 Network bandwidth constraints, data Networking-related metrics against, 216 coverage and capacity Network effectiveness, in SDNCC, (throughput), 34t, 36 217 deployment efficiency, 34t, 36–37 Network function virtualization energy efficiency, 34t, 37 (NFV), 2, 3, 220 QoS, 34t, 37 Index signaling delay and service latency, 34t, 37–38 spectral efficiency, 34t, 37 Network model proactive wireless edge caching model, 71–72 of SD-NCC, 200 3C joint optimization, 151–154, 152f video QoE model, 72–73 wireless communication model, 68–71 Network performance, 86–87 Network planning, 36 Network slicing See 5G cellular network slicing and caching Network usage cost, in SD-NCC, 206–207, 207f NFV See Network function virtualization (NFV) NLP See Nonlinear programming (NLP) No-caching scheme, 84 Nonlinear programming (NLP), 202–203 O Off-path caching, Online social networks (OSN), 17 On-path caching, OpenFlow, 3, 188, 220, 221 OPerating EXpenditure (OpEx), 3, 33, 37 Optimal deployment number, in SD-NCC, 208, 209f Optimality, 80–82, 84f OSN See Online social networks (OSN) P P2P networks See Peer-to-Peer (P2P) networks PE See Potential energy (PE) Peers, Peer-to-Peer (P2P) networks, 235 Penalty parameter, 165 Pending Interest Table (PIT), 189, 193 Performance metrics, 33–40, 35–36t Physical resource blocks (PRBs), 149–150 PIT See Pending Interest Table (PIT) Potential energy (PE), 134 Power spectrum density (PSD), 104 PRBs See Physical resource blocks (PRBs) Primal-dual interior-point method, 169, 171 Primal feasibility condition, 112, 169 Proactive wireless edge caching model, 71–72 Problem formulation, transformation and decomposition, 3C joint optimization, 158–164 Propagation delay, 37 PSD See Power spectrum density (PSD) Q QCI See QoS, class identifiers QoE-aware, 65 QoE guarantee, 88 QoS class identifiers (QCIs), 34t, 37 metric, 34t, 37 requirement, 11 Quality of service (QoS) See QoS Queuing delay, 38 R Radio access networks (RAN), 17, 18 Radio network controller (RNC), 19 RAN See Radio access networks (RAN) RAND See Random algorithm (RAND) Random algorithm (RAND), 139–144 Rayleigh fading, 114 236 Index Recovery time metric, 34t, 36 Relax binary variables, 160 Remote radio heads (RRHs), 17 Replicator dynamics method, 46 REQProxy, 14, 15 Resource allocation See also Resource allocation for 3C-HetNets; Virtualized multi-resources allocation of ADMM-based algorithm, 174f based on CRO algorithm, caching, 133–139, 136f caching/computing/bandwidth, 202–205 decentralized, in MEC system via ADMM, 171 scheme, in cognitive wireless networks, 46 strategies, networking/caching/computing, 210–211 via ADMM, 109–113 virtual network, 126 wireless cellular networks and, 150–151 Resource allocation for 3C-enabled HetNets architecture overview, 98–102 conclusions and future work, 121–122 introduction to, 96–97 simulation results and discussion, 113–121 via ADMM, 109–113 virtualized multi-resources allocation, 102–109 Resource management and task scheduling, in fog computing, 47 RESP ACK, 15 Response latency See Execution time metric Responses per request metric, 35t, 39 Revenue metric, 34t, 36 RNC See Radio network controller (RNC) Road side unit (RSU), 24 Rounding methods based on marginal benefits, 79–80 Round-trip time (RTT), 38 Routing model, of SD-NCC, 201 RRHs See Remote radio heads (RRHs) RSU See Road side unit (RSU) RTT See Round-trip time (RTT) S SBs See Small cell base stations (SBSs) Scalability, in networking, coaching, and computing integration, 12 Scalable controller design, in SD-NCC, 209–210 SDN See Software defined networking (SDN) SDNCC See Software defined networking, caching, and computing (SDNCC) SDWNs See Software-defined wireless networks (SDWNs) SE See Spectral efficiency (SE) SeNBs See Small cell eNodeBs (SeNBs) Serialization delay, 37–38 Server selection model, of SD-NCC, 201 Service latency metric, 34t, 37–38 Service provider (SP), 98, 100 Shannon bound, 104, 154 Signaling data, big, 221–222 Signaling delay metric, 34t, 37–38 Signaling monitoring system, 222 Signal-to-interference plus noise ratio (SINR), 71 Simulation parameters, 84t SINR See Signal-to-interference plus noise ratio (SINR) Index Small cell base stations (SBSs), 13, 20 Small cell eNodeBs (SeNBs), 151–152, 154 Social Butterfly, 17 Social Caches, 17 Software-defined and information-centric control, 190 Software defined networking (SDN), 2, 3, 65, 186, 188, 219–220 Software defined networking, caching, and computing (SDNCC) architecture of integrated framework, 191–199, 192f, 194f, 195f caching/computing/bandwidth resource allocation, 202–205 caching/computing model, 200 cloud and fog computing, 189–190 conclusions, 211 energy consumption cost, 207–208, 208f energy model, 201–202 information centric networking, 189 integrated program for, 190–191 introduction to, 186–187 local autonomy in data plane, 210 networking/caching/computing resource allocation strategies, 210–211 network model, 200 network usage cost, 206–207, 207f optimal deployment number, 208, 209f routing model, 201 scalable controller design, 209–210 server selection model, 201 software-defined networking, 188 237 Software-defined wireless networks (SDWNs), 65, 66 bandwidth provisioning of, 67 flow diagram in proposed cache-enabled flow control in, 83f implementation design in, 82–83 in-network caching, 68 SP See Service provider (SP) Spectral efficiency (SE), 3, 4, 34t, 37, 154 Spectrum allocation among UE, 174f Spectrum resource allocation mechanism, 149 Spectrum scarcity, Stability and convergence, in networking, coaching, and computing integration, 12 Stackelberg game model, 46 Static caching (baseline) scheme, 84 Statistical analysis and visualizations, 21 Stopping criterion, 112, 169 Streaming based service, audio and video, 8–9 Swiper framework, 46 System model and problem formulation, 68–75 notations, 69t wireless communication model, 68–71 T Third Generation Partnership Project (3GPP), 13 3C joint optimization communication model, 154 conclusions and future work, 179 introduction to, 149–151 network model, 151–154 problem formulation, transformation and decomposition, 158–164 problem solving via ADMM, 164–179 238 Index simulation results and discussion, 172–178 3D MIMO, 3GPP See Third Generation Partnership Project (3GPP) C-enabled virtualized HetNets, 99–101, 100f, 101f Throughput, 34t, 35t, 36, 40 Trace-driven analysis, 10 Traffic characteristics, 222 Traffic monitoring and analyzing, 222 Transcoding technology, 96 Transmission delay, 86–87 Transmission energy, in SD-NCC, 22 Transmission latency, of cloud computing systems, 46 Transmitting power, 36 U UE See User equipment (UE) Uninterruptable services against intermittent connectivity, in SDNCC, 216 Upper bound algorithm of wireless edging caching, 79 Use case, of integrated system of networking, caching and computing, 23–25, 24f User equipment (UE), 3, 5, computation resource allocation among, 174f MEC and, 150 spectrum allocation among, 174f time consumption vs size of computation task, 178f Utility function, 3C joint optimization, 156–158 V Vehicular fog computing, 47 Video mean opinion score (vMOS), 72, 85t, 88f Video QoE model, 72–73 Video-streaming, transmission delay as performance metric for, 86 Virtualized multi-resources allocation, 102–109 caching model, 105–106 computing model, 102–106 optimization problem, 106–107 optimization problem reformulation, 107–109 virtual heterogeneous networks model, 102 Virtual machines (VMs), 202 Virtual networks, 100–101, 101f Virtual radio resources, 98, 100 vMOS See Video mean opinion score (vMOS) VMs See Virtual machines (VMs) W WEC See Wireless edge caching (WEC) WiMAX See Worldwide interoperability for microwave access (WiMAX) Wireless cellular networks, 149 Wireless communication, signaling monitoring system for, 222 Wireless communication model, 68–71 Wireless edge caching (WEC), 66 Wireless edge caching (WEC) model, 71–72 Wireless local area networks (WLANs), 22 Wireless networks advances in, 2–3 conventional, problems in, Wireless network virtualization (WNV), 23, 96–97, 98, 221 Wireless SDN and edge caching, 66–68 Wireless sensor networks (WSNs), 45 WLANs See Wireless local area networks (WLANs) Index WNV See Wireless network virtualization (WNV) Workflow of Sd-NCC, 199f, 1988 Worldwide interoperability for microwave access (WiMAX), 22 WSNs See Wireless sensor networks (WSNs) 239 .. .Integrated Networking, Caching, and Computing Integrated Networking, Caching, and Computing F Richard Yu Tao Huang Yunjie Liu CRC Press Taylor... integration of networking, caching and computing 1.2.1 What is integration of networking, caching and computing? In order to give a clear description of integrated networking, caching and computing, ... 2.2.2 Computing- networking 2.2.2.1 Cloud computing and networking 2.2.2.2 Fog computing and networking 2.2.2.3 Mobile edge computing and networking 2.2.3 Caching -computing- networking