1  Cover

      2  IEEE Press

      3  Title Page

      4  Copyright

      5  Editor Biographies

      6  List of Contributors

      7  Preface

      8  Acknowledgments

      9  Acronyms

      10  Part I: Introduction 1 Overview of Network and Service Management 1.1 Network and Service Management at Large 1.2 Data Collection and Monitoring Protocols 1.3 Network Configuration Protocol 1.4 Novel Solutions and Scenarios Bibliography 2 Overview of Artificial Intelligence and Machine Learning 2.1 Overview 2.2 Learning Algorithms 2.3 Learning for Network and Service Management Bibliography Note

      11  Part II: Management Models and Frameworks 3 Managing Virtualized Networks and Services with Machine Learning 3.1 Introduction 3.2 Technology Overview 3.3 State‐of‐the‐Art 3.4 Conclusion and Future Direction Bibliography 4 Self‐Managed 5G Networks ¹ 4.1 Introduction 4.2 Technology Overview 4.3 5G Management State‐of‐the‐Art 4.4 Conclusions and Future Directions Bibliography Notes 5 AI in 5G Networks: Challenges and Use Cases 5.1 Introduction 5.2 Background 5.3 Case Studies 5.4 Conclusions and Future Directions Bibliography Note 6 Machine Learning for Resource Allocation in Mobile Broadband Networks 6.1 Introduction 6.2 ML in Wireless Networks 6.3 ML‐Enabled Resource Allocation 6.4 Conclusion and Future Directions Bibliography Note 7 Reinforcement Learning for Service Function Chain Allocation in Fog Computing 7.1 Introduction 7.2 Technology Overview 7.3 State‐of‐the‐Art 7.4 A RL Approach for SFC Allocation in Fog Computing 7.5 Evaluation Setup 7.6 Results 7.7 Conclusion and Future Direction Bibliography Note

      12  Part III: Management Functions and Applications 8 Designing Algorithms for Data‐Driven Network Management and Control: State‐of‐the‐Art and Challenges¹ 8.1 Introduction 8.2 Technology Overview 8.3 Data‐Driven Algorithm Design: State‐of‐the Art 8.4 Future Direction 8.5 Summary Acknowledgments Bibliography Note 9 AI‐Driven Performance Management in Data‐Intensive Applications 9.1 Introduction 9.2