Читать книгу Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning - Группа авторов - Страница 2

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 Data‐Processing Frameworks 9.3 State‐of‐the‐Art 9.4 Conclusion and Future Direction Bibliography Notes 10 Datacenter Traffic Optimization with Deep Reinforcement Learning 10.1 Introduction 10.2 Technology Overview 10.3 State‐of‐the‐Art: AuTO Design 10.4 Implementation 10.5 Experimental Results 10.6 Conclusion and Future Directions Bibliography Notes 11 The New Abnormal: Network Anomalies in the AI Era 11.1 Introduction 11.2 Definitions and Classic Approaches 11.3 AI and Anomaly Detection 11.4 Technology Overview 11.5 Conclusions and Future Directions Bibliography Notes 12 Automated Orchestration of Security Chains Driven by Process Learning* 12.1 Introduction 12.2 Related Work 12.3 Background 12.4 Orchestration of Security Chains 12.5 Learning Network Interactions 12.6 Synthesizing Security Chains 12.7 Verifying Correctness of Chains 12.8 Optimizing Security Chains 12.9 Performance Evaluation 12.10 Conclusions Bibliography Notes 13 Architectures for Blockchain‐IoT Integration¹ 13.1 Introduction 13.2 Blockchain‐IoT Integration (BIoT) 13.3 BIoT Architectures 13.4 Summary and Considerations Bibliography Note

13  Index

14  End User License Agreement