Adversarial Design Framework for Self-Driving Networks

Inspired by self-driving cars, the networking community is currently engaged in designing more automated and ``self-driving`` communication systems, aiming to overcome the cumbersome and error- prone manual approach to manage and operate networks. Ideally, such self-driving networks also allow to exploit the increasing flexibilities introduced by emerging new Internet technologies, such as software-defined and virtualized communication technologies. With these technologies, the networks allow to meet the stringent performance requirements of new applications (e.g., 5G, low-latency tele- operation, high-bandwidth machine-to-machine type communication, etc.), by adapting to the context and demand. The Internet, one of the largest and most complex artefacts built by mankind, has evolved organically over the last decades, and many design choices were taken based on experience and best practices. This project proposes a novel network framework to design and operate such networks, relying on the vision of self-driving networks, and studying how to integrate Machine Learning and Artificial Intelligence concepts into existing networks. In order to overcome the potential concerns regarding the dependability of Artificial Intelligence and Machine Learning approaches, we envision a hybrid solution which keeps the human in the loop. Hence, we first ask three fundamental questions in this project: how predictable are todays networks, i.e., user demands, workload traffic, and behavior of network functions? Can we make network design and algorithms data-driven and human interpretable? How to design a network framework that combines both generative workload models and data-driven algorithms with guarantees? The novelty of this project lies in the integration and application of Artificial Intelligence and Machine Learning on designing network algorithms. For the first time, Artificial Intelligence and Machine Learning should be integrated also in the testing and the developing phase of new networking solutions, and not only applied to solving problems. In terms of methodologies, we consider adversarial and game- theoretic approaches to test and optimize networks, to leverage the performance benefits from Machine Learning approaches while at the same time provide rigorous worst-case guarantees. Finally, a proof-of-concept implementation should demonstrate the new framework.

No additional funding sources recorded.