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

Discover the impact that new technologies are having on communication systems with this up-to-date and one-stop resource

Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning delivers a comprehensive overview of the impact of artificial intelligence (AI) and machine learning (ML) on service and network management. Beginning with a fulsome description of ML and AI, the book moves on to discuss management models, architectures, and frameworks. The authors also explore how AI and ML can be used in service management functions like the generation of workload profiles, service provisioning, and more.

The book includes a handpicked selection of applications and case studies, as well as a treatment of emerging technologies the authors predict could have a significant impact on network and service management in the future. Statistical analysis and data mining are also discussed, particularly with respect to how they allow for an improvement of the management and security of IT systems and networks. Readers will also enjoy topics like:

• A thorough introduction to network and service management, machine learning, and artificial intelligence
• An exploration of artificial intelligence and machine learning for management models, including autonomic management, policy-based management, intent based management, and network virtualization-based management
• Discussions of AI and ML for architectures and frameworks, including cloud systems, software defined networks, 5G and 6G networks, and Edge/Fog networks
• An examination of AI and ML for service management, including the automatic generation of workload profiles using unsupervised learning

Perfect for information and communications technology educators, Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning will also earn a place in the libraries of engineers and professionals who seek a structured reference on how the emergence of artificial intelligence and machine learning techniques is affecting service and network management.

Auteur

Nur Zincir-Heywood, PhD, is Full Professor of Computer Science with Dalhousie University in Nova Scotia, Canada. She is an Associate Editor of the IEEE Transactions on Network and Service Management and Wiley International Journal of Network Management. Marco Mellia, PhD, is Full Professor with Politecnico di Torino, Italy. He is an Associate Editor of the IEEE Transactions on Network and Service Management, Elsevier Computer Networks and ACM Computer Communication Reviews. Yixin Diao, PhD, is Director of Data Science and Analytics at PebblePost in New York, NY, USA. He is an Associate Editor of the IEEE Transactions on Network and Service Management and the Journal of Network and Systems Management.

Contenu

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 Guimarães, 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 Learn…