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
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
