Communication networks and service management in the era of artificial intelligence and machine learning

Communication networks and service management in the era of artificial intelligence and machine learning /

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Bibliographic Details
Corporate Author: ProQuest (Firm)
Other Authors: Zincir-Heywood, Nur (Editor), Mellia, Marco (Editor), Diao, Yixin, 1970- (Editor)
Format: Electronic eBook
Language:English
Published: Hoboken, New Jersey : John Wiley & Sons, Inc., [2021]
Series:IEEE Press series on networks and service management.
Subjects:
Computer networks.
Artificial intelligence.
Machine learning.
Computer Communication Networks
Artificial Intelligence
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)
Table of Contents:
  • List of Contributors xv
  • Preface xxi
  • Acknowledgments xxv
  • Acronyms xxvii
  • Part I Introduction 1
  • 1 Overview of Network and Service Management 3 Marco Mellia, Nur Zincir-Heywood, and Yixin Diao
  • 1.1 Network and Service Management at Large 3
  • 1.2 Data Collection and Monitoring Protocols 5
  • 1.2.1 SNMP Protocol Family 5
  • 1.2.2 Syslog Protocol 5
  • 1.2.3 IP Flow Information eXport (IPFIX) 6
  • 1.2.4 IP Performance Metrics (IPPM) 7
  • 1.2.5 Routing Protocols and Monitoring Platforms 8
  • 1.3 Network Configuration Protocol 9
  • 1.3.1 Standard Configuration Protocols and Approaches 9
  • 1.3.2 Proprietary Configuration Protocols 10
  • 1.3.3 Integrated Platforms for Network Monitoring 10
  • 1.4 Novel Solutions and Scenarios 12
  • 1.4.1 Software-Defined Networking - SDN 12
  • 1.4.2 Network Functions Virtualization -NFV 14
  • Bibliography 15
  • 2 Overview of Artificial Intelligence and Machine Learning 19 Nur Zincir-Heywood, Marco Mellia, and Yixin Diao
  • 2.1 Overview 19
  • 2.2 Learning Algorithms 20
  • 2.2.1 Supervised Learning 21
  • 2.2.2 Unsupervised Learning 22
  • 2.2.3 Reinforcement Learning 23
  • 2.3 Learning for Network and Service Management 24
  • Bibliography 26
  • Part II Management Models and Frameworks 33
  • 3 Managing Virtualized Networks and Services with Machine Learning 35 Raouf Boutaba, Nashid Shahriar, Mohammad A. Salahuddin, and Noura Limam
  • 3.1 Introduction 35
  • 3.2 Technology Overview 37
  • 3.2.1 Virtualization of Network Functions 38
  • 3.2.1.1 Resource Partitioning 38
  • 3.2.1.2 Virtualized Network Functions 40
  • 3.2.2 Link Virtualization 41
  • 3.2.2.1 Physical Layer Partitioning 41
  • 3.2.2.2 Virtualization at Higher Layers 42
  • 3.2.3 Network Virtualization 42
  • 3.2.4 Network Slicing 43
  • 3.2.5 Management and Orchestration 44
  • 3.3 State-of-the-Art 46
  • 3.3.1 Network Virtualization 46
  • 3.3.2 Network Functions Virtualization 49
  • 3.3.2.1 Placement 49
  • 3.3.2.2 Scaling 52
  • 3.3.3 Network Slicing 55
  • 3.3.3.1 Admission Control 55
  • 3.3.3.2 Resource Allocation 56
  • 3.4 Conclusion and Future Direction 59
  • 3.4.1 Intelligent Monitoring 60
  • 3.4.2 Seamless Operation and Maintenance 60
  • 3.4.3 Dynamic Slice Orchestration 61
  • 3.4.4 Automated Failure Management 61
  • 3.4.5 Adaptation and Consolidation of Resources 61
  • 3.4.6 Sensitivity to Heterogeneous Hardware 62
  • 3.4.7 Securing Machine Learning 62
  • Bibliography 63
  • 4 Self-Managed 5G Networks 69 Jorge Mar̕tn-̌Prez, Lina Magoula, Kiril Antevski, Carlos Guimarês, Jorge Baranda, Carla Fabiana Chiasserini, Andrea Sgambelluri, Chrysa Papagianni, Andřs Gar̕ca-Saavedra, Ricardo Mar̕tnez, Francesco Paolucci, Sokratis Barmpounakis, Luca Valcarenghi, Claudio EttoreCasetti, Xi Li, Carlos J. Bernardos, Danny De Vleeschauwer, Koen De Schepper, Panagiotis Kontopoulos, Nikolaos Koursioumpas, Corrado Puligheddu, Josep Mangues-Bafalluy, and Engin Zeydan
  • 4.1 Introduction 69
  • 4.2 Technology Overview 73
  • 4.2.1 RAN Virtualization and Management 73
  • 4.2.2 Network Function Virtualization 75
  • 4.2.3 Data Plane Programmability 76
  • 4.2.4 Programmable Optical Switches 77
  • 4.2.5 Network Data Management 78
  • 4.3 5G Management State-of-the-Art 80
  • 4.3.1 RAN resource management 80
  • 4.3.1.1 Context-Based Clustering and Profiling for User and Network Devices 80
  • 4.3.1.2 Q -Learning Based RAN Resource Allocation 81
  • 4.3.1.3 vrAIn: AI-Assisted Resource Orchestration for Virtualized Radio Access Networks 81
  • 4.3.2 Service Orchestration 83
  • 4.3.3 Data Plane Slicing and Programmable Traffic Management 85
  • 4.3.4 Wavelength Allocation 86
  • 4.3.5 Federation 88
  • 4.4 Conclusions and Future Directions 89
  • Bibliography 92
  • 5 AI in 5G Networks: Challenges and Use Cases 101 Stanislav Lange, Susanna Schwarzmann, Marija Gajiþc, Thomas Zinner, and Frank A. Kraemer
  • 5.1 Introduction 101
  • 5.2 Background 103
  • 5.2.1 ML in the Networking Context 103
  • 5.2.2 ML in Virtualized Networks 104
  • 5.2.3 ML for QoE Assessment and Management 104
  • 5.3 Case Studies 105
  • 5.3.1 QoE Estimation and Management 106
  • 5.3.1.1 Main Challenges 107
  • 5.3.1.2 Methodology 108
  • 5.3.1.3 Results and Guidelines 109
  • 5.3.2 Proactive VNF Deployment 110
  • 5.3.2.1 Problem Statement and Main Challenges 111
  • 5.3.2.2 Methodology 112
  • 5.3.2.3 Evaluation Results and Guidelines 113
  • 5.3.3 Multi-service, Multi-domain Interconnect 115
  • 5.4 Conclusions and Future Directions 117
  • Bibliography 118
  • 6 Machine Learning for Resource Allocation in Mobile Broadband Networks 123 Sadeq B. Melhem, Arjun Kaushik, Hina Tabassum, and Uyen T. Nguyen
  • 6.1 Introduction 123
  • 6.2 ML in Wireless Networks 124
  • 6.2.1 Supervised ML 124
  • 6.2.1.1 Classification Techniques 125
  • 6.2.1.2 Regression Techniques 125
  • 6.2.2 Unsupervised ML 126
  • 6.2.2.1 Clustering Techniques 126
  • 6.2.2.2 Soft Clustering Techniques 127
  • 6.2.3 Reinforcement Learning 127
  • 6.2.4 Deep Learning 128
  • 6.2.5 Summary 129
  • 6.3 ML-Enabled Resource Allocation 129
  • 6.3.1 Power Control 131
  • 6.3.1.1 Overview 131
  • 6.3.1.2 State-of-the-Art 131
  • 6.3.1.3 Lessons Learnt 132
  • 6.3.2 Scheduling 132
  • 6.3.2.1 Overview 132
  • 6.3.2.2 State-of-the-Art 132
  • 6.3.2.3 Lessons Learnt 134
  • 6.3.3 User Association 134
  • 6.3.3.1 Overview 134
  • 6.3.3.2 State-of-the-Art 136
  • 6.3.3.3 Lessons Learnt 136
  • 6.3.4 Spectrum Allocation 136
  • 6.3.4.1 Overview 136
  • 6.3.4.2 State-of-the-Art 138
  • 6.3.4.3 Lessons Learnt 138
  • 6.4 Conclusion and Future Directions 140
  • 6.4.1 Transfer Learning 140
  • 6.4.2 Imitation Learning 140
  • 6.4.3 Federated-Edge Learning 141
  • 6.4.4 Quantum Machine Learning 142
  • Bibliography 142
  • 7 Reinforcement Learning for Service Function Chain Allocation in Fog Computing 147 Još Santos, Tim Wauters, Bruno Volckaert, and Filip De Turck
  • 7.1 Introduction 147
  • 7.2 Technology Overview 148
  • 7.2.1 Fog Computing (FC) 149
  • 7.2.2 Resource Provisioning 149
  • 7.2.3 Service Function Chaining (SFC) 150
  • 7.2.4 Micro-service Architecture 150
  • 7.2.5 Reinforcement Learning (RL) 151
  • 7.3 State-of-the-Art 152
  • 7.3.1 Resource Allocation for Fog Computing 152
  • 7.3.2 ML Techniques for Resource Allocation 153
  • 7.3.3 RL Methods for Resource Allocation 154
  • 7.4 A RL Approach for SFC Allocation in Fog Computing 155
  • 7.4.1 Problem Formulation 155
  • 7.4.2 Observation Space 156
  • 7.4.3 Action Space 157
  • 7.4.4 Reward Function 158
  • 7.4.5 Agent 161
  • 7.5 Evaluation Setup 162
  • 7.5.1 Fog-Cloud Infrastructure 162
  • 7.5.2 Environment Implementation 162
  • 7.5.3 Environment Configuration 164
  • 7.6 Results 165
  • 7.6.1 Static Scenario 165
  • 7.6.2 Dynamic Scenario 167
  • 7.7 Conclusion and Future Direction 169
  • Bibliography 170
  • Part III Management Functions and Applications 175
  • 8 Designing Algorithms for Data-Driven Network Management and Control: State-of-the-Art and Challenges 177 Andreas Blenk, Patrick Kalmbach, Johannes Zerwas, and Stefan Schmid
  • 8.1 Introduction 177
  • 8.1.1 Contributions 179
  • 8.1.2 Exemplary Network Use Case Study 179
  • 8.2 Technology Overview 181
  • 8.2.1 Data-Driven Network Optimization 181
  • 8.2.2 Optimization Problems over Graphs 182
  • 8.2.3 From Graphs to ML/AI Input 184
  • 8.2.4 End-to-End Learning 187
  • 8.3 Data-Driven Algorithm Design: State-of-the Art 188
  • 8.3.1 Data-Driven Optimization in General 188
  • 8.3.2 Data-Driven Network Optimization 190
  • 8.3.3 Non-graph Related Problems 192
  • 8.4 Future Direction 193
  • 8.4.1 Data Production and Collection 193
  • 8.4.2 ML and AI Advanced Algorithms for Network Management with Performance Guarantees 194
  • 8.5 Summary 194
  • Acknowledgments 195
  • Bibliography 195
  • 9 AI-Driven Performance Management in Data-Intensive Applications 199 Ahmad Alnafessah, Gabriele Russo Russo, Valeria Cardellini, Giuliano Casale, and Francesco Lo Presti
  • 9.1 Introduction 199
  • 9.2 Data-Processing Frameworks 200
  • 9.2.1 Apache Storm 200
  • 9.2.2 Hadoop MapReduce 201
  • 9.2.3 Apache Spark 202
  • 9.2.4 Apache Flink 202
  • 9.3 State-of-the-Art 203
  • 9.3.1 Optimal Configuration 203
  • 9.3.1.1 Traditional Approaches 203
  • 9.3.1.2 AI Approaches 204
  • 9.3.1.3 Example: AI-Based Optimal Configuration 206
  • 9.3.2 Performance Anomaly Detection 207
  • 9.3.2.1 Traditional Approaches 208
  • 9.3.2.2 AI Approaches 208
  • 9.3.2.3 Example: ANNs-Based Anomaly Detection 210
  • 9.3.3 Load Prediction 211
  • 9.3.3.1 Traditional Approaches 212
  • 9.3.3.2 AI Approaches 212
  • 9.3.4 Scaling Techniques 213
  • 9.3.4.1 Traditional Approaches 213
  • 9.3.4.2 AI Approaches 214
  • 9.3.5 Example: RL-Based Auto-scaling Policies 214
  • 9.4 Conclusion and Future Direction 216
  • Bibliography 217
  • 10 Datacenter Traffic Optimization with Deep Reinforcement Learning 223 Li Chen, Justinas Lingys, Kai Chen, and Xudong Liao
  • 10.1 Introduction 223
  • 10.2 Technology Overview 225
  • 10.2.1 Deep Reinforcement Learning (DRL) 226
  • 10.2.2 Applying ML to Networks 227
  • 10.2.3 Traffic Optimization Approaches in Datacenter 229
  • 10.2.4 Example: DRL for Flow Scheduling 230
  • 10.2.4.1 Flow Scheduling Problem 230
  • 10.2.4.2 DRL Formulation 230
  • 10.2.4.3 DRL Algorithm 231
  • 10.3 State-of-the-Art: AuTO Design 231
  • 10.3.1 Problem Identified 231
  • 10.3.2 Overview 232
  • 10.3.3 Peripheral System 233
  • 10.3.3.1 Enforcement Module 233
  • 10.3.3.2 Monitoring Module 234
  • 10.3.4 Central System 234
  • 10.3.5 DRL Formulations and Solutions 235
  • 10.3.5.1 Optimizing MLFQ Thresholds 235
  • 10.3.5.2 Optimizing Long Flows 239
  • 10.4 Implementation 239
  • 10.4.1 Peripheral System 239
  • 10.4.1.1 Monitoring Module (MM): 240
  • 10.4.1.2 Enforcement Module (EM): 240
  • 10.4.2 Central System 241
  • 10.4.2.1 sRLA 241
  • 10.4.2.2 lRLA 242
  • 10.5 Experimental Results 242
  • 10.5.1 Setting 243
  • 10.5.2 Comparison Targets 244
  • ...

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