An effort to democratize networking research in the era of AI/ML

Abstract

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. In this paper, 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, because of their ubiquity, help create a more level playing field for academic researchers. Their various limitations notwithstanding, as real-world production networks, such enterprise networks can (i) serve as unique sources for some of the rich data that will enable these 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 new research 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 and evaluation of AI/ML-based tools to the point where they can be deployed in practice.