Machine Learning for Network Management
Description
This book details how machine learning (ML) can be used in network management. Geared towards an upper undergraduate or graduate level class in machine learning or computer networks, the author showcases the intersection between ML/artificial intelligence (AI) and network management paradigms. The book includes practical methodologies and guidelines to realizing AI/ML-driven network management. The book also provides a landscape of AI/ML techniques more relevant to network management while also presenting AI/ML-based network management solutions deployed in production environments so students can explore applications. The book features homework problems, exercises, case studies, and PowerPoint slides.
More details
Person
Oscar Mauricio Caicedo Rendón is a full professor at the Universidad del Cauca (Unicauca), Colombia, where he is a member of the Telematics Engineering Group of the Faculty of Telecommunications Engineering. He received his Ph.D. in Computer Science from the Federal University of Rio Grande do Sul, Brazil (2015), and his M.Sc. in Telematics Engineering (2006) as well as his degree in Electronics and Telecommunications Engineering (2001) from Unicauca. He is an IEEE Senior Member (since 2020) and has been classified as a Senior Researcher by MinCiencias Colombia since 2018. He has also served as a visiting professor at the University of Campinas (Unicamp), Brazil, in 2018 and 2022. Prof. Caicedo Rendón is Series Editor for the IEEE Communications Magazine Series on Network Softwarization and Management, and an Associate Editor for IEEE Networking Letters, the IEEE Internet of Things Journal, and the IEEE Latin American Transactions. He served as General Chair of the IEEE Colombian Conference on Communications and Computing and Technical Program Co-Chair for IEEE LATINCOM 2020 and for the Machine Learning for Communications and Networking Symposium at IEEE GLOBECOM 2022. He continues to participate as a TPC member in various IEEE-sponsored international conferences and workshops, including NetSoft, CNMS, NOMS, EuCNC, and CloudNet, as well as reviewer in important venues like IEEE Transactions on Network and Service Management, IEEE Transactions on Network Science and Engineering, and IEEE Transactions on Cognitive Communications and Networking, His current research interests include network and service management, network functions virtualization, software-defined networking, machine learning for networking, and network softwarization.