Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning. Группа авторов
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target="_blank" rel="nofollow" href="#ulink_47dea42a-b4e8-538d-884a-00db058e6a90">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
List of Tables
1 Chapter 3Table 3.1 Summary of the state‐of‐the‐art for virtual network embedding.Table 3.2 Summary of the state‐of‐the‐art for ML‐based placement in NFV.Table 3.3 Summary of the state‐of‐the‐art for ML‐based scaling in NFV.Table 3.4 Summary of the state‐of‐the‐art for ML‐based admission control app...Table 3.5 Summary of the state‐of‐the‐art for ML‐based resource allocation a...
2 Chapter 5Table 5.1 Categorization of covered use cases.Table 5.2 Impact of including different monitoring types on QoE estimation a...
3 Chapter 6Table 6.1 Summary of ML techniques.Table 6.2 Machine learning‐based power control.Table 6.3 blackMachine‐learning based scheduling.Table 6.4 Machine learning‐based user association.Table 6.5 Machine learning‐based spectrum allocation.
4 Chapter 7Table 7.1 Variables used for the minimization of the overall system cost.Table 7.2 A sample fraction of the observation space of the gym‐fog environm...Table 7.3 A sample fraction of the action space of the gym‐fog environment.Table 7.4 The reduced observation space of the gym‐fog environment.Table 7.5 The hardware configuration of each node.Table 7.6 The gym‐fog environment configuration.Table 7.7 The MILP model execution time.
5 Chapter 8Table 8.1 Node and graph features used for problem representation and learni...Table 8.2 provides overview of research work, general optimization problems ...Table 8.3 overviewing networking research work, general optimization problem...
6 Chapter 9Table 9.1 Summary of the key characteristics of data‐processing platform.Table 9.2 Summary of the AI techniques considered for performance management...
7 Chapter 11Table 11.1 Summary of reviewed papers.Table 11.2 Summary of the reviewed tools.
8 Chapter 12Table 12.1 The set of Android applications considered for evaluation.Table 12.2 Number of rules for combined chains.Table 12.3 Accuracy of chains generated for protecting applications.
9 Chapter 13Table 13.1 Performance of selected cryptographic functions on different IoT ...
List of Illustrations
1 Chapter 1Figure 1.1 Network and service management at large.Figure 1.2 Example of monitoring architecture.Figure 1.3 The SDN architecture.Figure 1.4 Network functions virtualization architecture.
2 Chapter 2Figure 2.1 Supervised learning model.Figure 2.2 Unsupervised learning model.Figure 2.3 Reinforcement learning model.
3 Chapter 3Figure 3.1 Technologies for virtualizing network functions with examples.