Research Area: Software Defined Networks
With the exponential growth in technology performance, the modern world has become highly connected, digitized, and diverse. Within this hyper-connected world, Communication networks or the Internet are part of our daily life and play many important roles. However, the ever-growing internet services, application, and massive traffic growth complexity networks that reach a point where traditional management functions mainly govern by human operations fail to keep the network operational. In this context, Software-Defined Networking (SDN) emerge as a new architecture for network management.
The management operations will leverage the ML ability to exploit hidden pattern in data to create knowledge. This association SDN-AI/ML, with the promise to simplify network management, needs many challenges to be addresses. Self-driving networking or full network automation is the "Holy Grail" of this association. In this thesis, two of the concerned challenges retain our attention. Firstly, efficient data collection with SDN, especially real-time telemetry. For this challenge, we propose COCO for Confidence-based Collection, a low overhead near-real-time data collection in SDN.
These ML-based management schemes are built upon SDN, leveraging its centralized global view, telemetry capabilities, and management flexibility. The effectiveness of our efficient data collection framework and the machine-learning-based performance optimization show promising results. We expect that improved SDN monitoring with AI/ML analytics capabilities can considerably augment network management and make a big step in the self-driving network journey.
Name of the Researcher: Kokouvi Benoit Nougnanke
Name of the Supervisor(s): Yann Labit
Year of Completion: 2021
University: University of Paul Sabatier
Thesis Link: Home Page Url
