Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning
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- Название:Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning
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Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning: краткое содержание, описание и аннотация
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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.
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9 Chapter 9Figure 9.1 Comparison between Bayesian optimization and random search to rea...Figure 9.2 A taxonomy of anomaly detection techniques generalizing the ones ...Figure 9.3 The performance of ANNs models that is trained with training data...Figure 9.4 Streaming workload in a simulated experiment (a) and average rewa...
10 Chapter 10Figure 10.1 A general reinforcement learning setting using neural network as...Figure 10.2 Current DRL systems are insufficient.Figure 10.3 AuTO overview.Figure 10.4 Multi‐level feedback queuing.Figure 10.5 AuTO: A four‐queue example.Figure 10.6 Comparison of deep stochastic and deep deterministic policies.Figure 10.7 Testbed topology.Figure 10.8 Traffic distributions in evaluation.Figure 10.9 Homogeneous traffic: Average and p99 FCT.Figure 10.10 Spatially heterogeneous traffic: Average and p99 FCT.Figure 10.11 Dynamic scenarios: average FCT.Figure 10.12 Dynamic scenarios: p99 FCT.Figure 10.13 MLFQ thresholds from sRLA vs. optimal thresholds.Figure 10.14 Average FCT using MLFQ thresholds from sRLA vs. optimal thresho...Figure 10.15 p99 FCT using MLFQ thresholds from sRLA vs. optimal thresholds....Figure 10.16 Load balancing using lRLA (PG algorithm): difference in number ...Figure 10.17 CS response latency: Traces from four runs.Figure 10.18 CS response latency: Scaling short flows ( 
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11 Chapter 11Figure 11.1 A taxonomy for anomaly detection problems and techniques.Figure 11.2 Examples of anomaly macro‐categories. (a) Pointwise; (b) Context...Figure 11.3 Example of deep neural network with four hidden layers.Figure 11.4 Basic autoencoder structure.Figure 11.5 Basic generative adversarial network architecture.
12 Chapter 12Figure 12.1 Example of a Kinetic control plane automaton.Figure 12.2 Security chain orchestrator integrated into a SDN infrastructure...Figure 12.3 Different steps of the proposed methodology.Figure 12.4 Inferred Markov chain of the Pokemon Go Android application.Figure 12.5 A toy security chain in pyretic and its encoding as a constraint...Figure 12.6 Overhead in terms of bandwidth introduced by security chain depl...
13 Chapter 13Figure 13.1 IoT adoption estimation for the 2017–2030 period [1].Figure 13.2 Categories of BIoT risks.Figure 13.3 BC‐IoT integration (BIoT) metrics.Figure 13.4 BC and IoT integration models [22]. (a) IoT–IoT, (b) IoT–Blockch...Figure 13.5 An SDN‐enabled fog and cloud‐based BIoT architecture.Figure 13.6 BIIT 1.0 architecture.
Guide
1 Cover Page
2 IEEE Press IEEE Press 445 Hoes Lane Piscataway, NJ 08854 IEEE Press Editorial Board Ekram Hossain, Editor in Chief Jón Atli Benediktsson Xiaoou Li Jeffrey Reed Anjan Bose Lian Yong Diomidis Spinellis David Alan Grier Andreas Molisch Sarah Spurgeon Elya B. Joffe Saeid Nahavandi Ahmet Murat Tekalp
3 Title Page Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning Edited by Nur Zincir–Heywood, Marco Mellia, and Yixin Diao
4 Copyright
5 Editor Biographies
6 List of Contributors
7 Preface
8 Acknowledgments
9 Acronyms
10 Table of Contents
11 Begin Reading
12 Index
13 WILEY END USER LICENSE AGREEMENT
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