The growing complexity of communication networks and the explosion of network traffic have made the task of managing these networks exceedingly hard. A potential approach for striking this increasing complexity is to build an autonomous self-driving network that can measure, analyze and control itself in real time and in an automated fashion without direct human intervention. In this thesis, we focus on realizing such autonomous networks leveraging state-of-the-art networking technologies along with artificial intelligence and machine learning techniques. Toward this goal, we exploit different learning paradigms to automate network management. First, we propose supervised machine learning methods to detect increases in delays in mobile broadband networks. Further, considering the challenges of supervised learning in networking applications, we present a novel real-time distributed architecture for detecting anomalies in mobile network data in an unsupervised fashion.
- Anna Brunström, Professor of Computer Science, Karlstad University, Karlstad, Sweden
- Marco Fiore, Research Professor, IMDEA Networks Institute, Madrid, Spain
- Boning Feng, Associate Professor, IT-TKD- OsloMet, Oslo, Norway
- Main supervisor: Ahmed Almokashfi, Principal Reasearch Scientist at Amazon & previous Research Professor at SimulaMet
- Michael Riegler, Chief Research Scientist at SimulaMet
Chair of defense
- André Brodtkorb