A growing concern within today's networking community is that with the proliferation of Artificial Intelligence/Machine Learning (AI/ML) techniques, a lack of access to real-world production networks is putting academic researchers at a significant disadvantage. Indeed, compared to a select few research groups in industry that can leverage access to their global-scale production networks in their data-driven efforts to develop and evaluate learning models, academic researchers not only struggle to get their hands on real-world data sets but find it almost impossible to adequately train and assess their learning models under realistic conditions.
This talk outlines a vision for democratizing networking research in the era of AI/ML. In particular, we argue that when appropriately instrumented and properly managed, enterprise networks in the form of university or campus networks can serve as real-world production networks and can be leveraged to (i) serve as unique sources for some of the rich data that will enable academic researchers to influence or advance the current state-of-the-art in AI/ML for networking and (ii) also function as much-needed test beds where newly developed AI/ML-based tools can be evaluated or "road-tested" prior to their actual deployment in the production network. We discuss the different challenges that arise from this proposed dual role of campus networks and comment on the opportunities our proposal affords for both academic and industry researchers to benefit from the advantages and limitations of their respective production environments in their common quest to advance the development of AI/ML-based network automation tools to the point where they can be deployed in practice.
Walter Willinger is Chief Scientist at NIKSUN, Inc., the world leader in real-time monitoring and cyber forensics solutions. Before joining NIKSUN, he worked at AT&T Labs-Research in Florham Park, NJ from 1996 to 2013 and at Bellcore Applied Research from 1986 to 1996. He received his Dipl. Math. from the ETH Zurich and his M.S. and Ph.D. in Operations Research and Industrial Engineering from Cornell University. He is a Fellow of ACM (2005), Fellow of IEEE (2005), AT&T Fellow (2007), and Fellow of SIAM (2009), co-recipient of the 1995 IEEE Communications Society W.R. Bennett Prize Paper Award and the 1996 IEEE W.R.G. Baker Prize Award, and co-recipient of the 2005 and 2016 ACM/SIGCOMM Test-of-Time Paper Awards. His paper "On the Self-Similar Nature of Ethernet Traffic" is featured in "The Best of the Best - Fifty Years of Communications and Networking Research," a 2007 IEEE Communications Society book compiling the most outstanding papers published in the communications and networking field in the last half century.