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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49 49 Makanju, A., Zincir‐Heywood, N., and Milios, E.E. (2013). Investigating event log analysis with minimum apriori information. 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013), pp. 962–968.
50 50 Jiang, H., Zhang, J.J., Gao, W., and Wu, Z. (2014). Fault detection, identification, and location in smart grid based on data‐driven computational methods. IEEE Transactions on Smart Grid 5 (6): 2947–2956. https://doi.org/10.1109/TSG.2014.2330624.
51 51 Uriarte, R.B., Tiezzi, F., and Tsaftaris, S.A. (2016). Supporting autonomic management of clouds: service clustering with random forest. IEEE Transactions on Network and Service Management 13 (3): 595–607. https://doi.org/10.1109/TNSM.2016.2569000.
52 52 Fadlullah, Z.M., Tang, F., Mao, B. et al. (2017). State‐of‐the‐art deep learning: evolving machine intelligence toward tomorrow's intelligent network traffic control systems. IEEE Communication Surveys and Tutorials 19 (4): 2432–2455. https://doi.org/10.1109/COMST.2017.2707140.
53 53 Messager, A., Parisis, G., Kiss, I.Z. et al. (2019). Inferring functional connectivity from time‐series of events in large scale network deployments. IEEE Transactions on Network and Service Management 16 (3): 857–870. https://doi.org/10.1109/TNSM.2019.2932896.
54 54 Tiwana, M.I., Sayrac, B., and Altman, Z. (2010). Statistical learning in automated troubleshooting: application to lte interference mitigation. IEEE Transactions on Vehicular Technology 59 (7): 3651–3656. https://doi.org/10.1109/TVT.2010.2050081.
55 55 Ahmed, J., Josefsson, T., Johnsson, A. et al. (2018). Automated diagnostic of virtualized service performance degradation. NOMS 2018 – 2018 IEEE/IFIP Network Operations and Management Symposium, pp. 1–9. https://doi.org/10.1109/NOMS.2018.8406234.
56 56 Renga, D., Apiletti, D., Giordano, D. et al. (2020). Data‐driven exploratory models of an electric distribution network for fault prediction and diagnosis. Computing 102 (5): 1199–1211. https://doi.org/10.1007/s00607‐019‐00781‐w.
57 57 Steenwinckel, B., Paepe, D.D., Hautte, S.V. et al. (2021). FLAGS: a methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning. Future Generation Computer Systems 116: 30–48. https://doi.org/10.1016/j.future.2020.10.015.
58 58 Xie, J., Yu, F.R., Huang, T. et al. (2018). A survey of machine learning techniques applied to software defined networking (SDN): research issues and challenges. IEEE Communication Surveys and Tutorials 21 (1): 393–430.
59 59 Zhang, C., Patras, P., and Haddadi, H. (2019). Deep learning in mobile and wireless networking: a survey. IEEE Communication Surveys and Tutorials. 21 (3)
60 60 Park, S., Kim, H., Hong, J. et al. (2020). Machine learning‐based optimal VNF deployment. 21st Asia‐Pacific Network Operations and Management Symposium, APNOMS 2020, Daegu, South Korea (22–25 September 2020), IEEE, pp. 67–72. https://doi.org/10.23919/APNOMS50412.2020.9236970.
61 61 Lerner, A. (2017). Intent‐based networking. Gartner Blog:
https://blogs.gartner.com/andrew-lerner/2017/02/07/intent-based-networking/(accessed 15 April 2021).62 62 ETSI (2020). Zero‐touch network and Service Management. https://www.etsi.org/technologies/zero-touch-network-service-management(accessed 13 April 2021).
63 63 Tsvetkov, T., Ali‐Tolppa, J., Sanneck, H., and Carle, G. (2016). Verification of configuration management changes in self‐organizing networks. IEEE Transactions on Network and Service Management 13 (4): 885–898. https://doi.org/10.1109/TNSM.2016.2589459.
64 64 Zhang, Y., Yao, J., and Guan, H. (2017). Intelligent cloud resource management with deep reinforcement learning. IEEE Cloud Computing 4 (6): 60–69. https://doi.org/10.1109/MCC.2018.1081063.
65 65 Mismar, F.B., Choi, J., and Evans, B.L. (2019). A framework for automated cellular network tuning with reinforcement learning. IEEE Transactions on Communications 67 (10): 7152–7167. https://doi.org/10.1109/TCOMM.2019.2926715.
66 66 Yao, H., Mai, T., Jiang, C. et al. (2019). Ai routers network mind: a hybrid machine learning paradigm for packet routing. IEEE Computational Intelligence Magazine 14 (4): 21–30. https://doi.org/10.1109/MCI.2019.2937609.
67 67 Zhang, Q., Wang, X., Lv, J., and Huang, M. (2020). Intelligent content‐aware traffic engineering for SDN: an Ai‐driven approach. IEEE Network 34 (3): 186–193. https://doi.org/10.1109/MNET.001.1900340.
68 68 Zhang, J., Ye, M., Guo, Z. et al. (2020). CFR‐RL: traffic engineering with reinforcement learning in SDN. IEEE Journal on Selected Areas in Communications 38 (10): 2249–2259. https://doi.org/10.1109/JSAC.2020.3000371.
69 69 Le, D.C. and Zincir‐Heywood, N. (2020). A frontier: dependable, reliable and secure machine learning for network/system management. Journal of Network and Systems Management 28 (4): 827–849.
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1 1 Subject to other limitations, such as the curse of dimensionality.
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