Abstract: Network Intelligence course encompasses three main parts. In the first part of the course, we aim to present the background on Network Intelligence i.e. the latest state of the art on Artificial Intelligence (AI) for network and service management. In the second part of the course, we will delve into Machine Learning (ML) for network data analysis. We will be introducing and discussing various techniques for specific problems (prediction, classification, clustering, etc.), as well as relevant pointers to consider in the domain. In the third part, we will present a hands-on demonstration through a notebook capturing the end-to-end process of leveraging ML in a network management use case (forecasting of network data, anomaly detection, etc.). The demonstration will make use of real network data. The participants will be presented with:
- An overview of past literature works and ongoing efforts on applying ML for network operations and management.
- A concise course on applying ML for network data analytics with relevant pointers and useful takeaways.
- Insights about best environments to use (opensource libraries and frameworks)
- A hands-on demonstration showcasing ML techniques applied to a network management use case scenario that involves real network traces.
Teaching and hands-on experience of the instructors will be leveraged for best possible outcome.
Imen Grida Ben Yahia is currently with Orange Labs, France, as a Research Project Leader on Autonomic & Cognitive Management and Expert in Future Networks. She is also leading the international initiative within IEEE comsoc on “Network Intelligence” http://ni.committees.comsoc.org/. She received her PhD degree in Telecommunication Networks from Pierre et Marie Curie University in conjunction with Télécom SudParis in 2008. Her current research interests are autonomic and cognitive management for software and programmable networks that include artificial intelligence for SLA and fault management, knowledge and abstraction for management operations, intent- and policy-based management. She contributed to several European research projects like Servery, FP7 UniverSelf, the H2020 CogNet and currently the 5G SliceNet. Imen is teaching Cognitive Network Management in Telecom Sud Paris, a module of (6 to 12) hours per year including Network management operations challenges, use case for Cognitive Network Management (AI based) and hands-on showcasing machine learning for real network data.
Noura Limam received the M.Sc. and Ph.D. degrees in computer science from the University Pierre et Marie Curie, Paris VI, in 2002 and 2007, respectively. She is currently a research assistant professor of computer science at the University of Waterloo, Canada, where she has been teaching 400- and 600-level courses in computer networks and distributed systems (CS456/CS656, CS454/CS654, and CS436/CS636) with combined theory and hands-on training, for the last several years. She is on the technical program committee and organization committee of several IEEE conferences. Her contributions are in the area of network and service management. Her current research interests are in autonomic networking and cognitive network management.