Includes bibliographical references and index.

"The book provides a structured reference on how the emergence of artificial intelligence (AI)/machine learning (ML) techniques is changing the landscape in strategic areas within service and network management. It begins with overviews of network and service management and ML and AI before moving on to discuss management models, architectures, and frameworks. Later chapters explore how AI and ML can be used in service management functions such as the generation of workload profiles, service provisioning, and more. The book closes with a selection of applications and case studies and speculation on enabling and emerging technologies."-- Provided by publisher.

Description based on print version record and CIP data provided by publisher; resource not viewed.

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 Martín-Pérez, Lina Magoula, Kiril Antevski, Carlos Guimarês, Jorge Baranda, Carla Fabiana Chiasserini, Andrea Sgambelluri, Chrysa Papagianni, Andrés García-Saavedra, Ricardo Martínez, 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 Gaji´c, 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 José 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 -- ...

Wiley Frontlist Obook All English 2021