CC* Integration-Large: Democratizing Networking Research in the Era of AI/ML

点击翻译按钮获取中文摘要

The emerging area of self-driving networks provides network administrators at campus networks to automate most network-management tasks. Such an automation ensures that the network remains performant and reliable amidst various disruptions, while requiring minimal interventions from the network administrators. However, making significant contributions to self-driving network research requires developing artificial intelligence (AI) and machine learning (ML)-based tools and demonstrating that they work in practice. Unfortunately, in stark contrast to their counterparts in industry, most academic researchers have neither access to the proper data for developing learning-based tools nor have properly instrumented testbeds for road-testing the resulting tools in realistic settings. This collaborative project brings together investigators from the University of California-Santa Barbara, University of Chicago, and NIKSUN Inc., to investigate how to use campus networks to overcome barriers to self-driving network research. First, it will deploy packet-processing pipelines at two campus networks to collect the proper network data at scale without compromising user privacy. It will then strategically place programmable network devices at campus networks to safely road-test newly developed learning models in production settings. Finally, it will illustrate the capabilities enabled by these newly-instrumented campus networks for developing, evaluating, and road-testing new learning models with different use cases.This project is intended to seed a community effort that uses campus networks as vehicles for democratizing self-driving networks research, improving its transparency through reproducibility, and ensuring its success in practice by establishing trust. As such, it promises to be transformative not only for the network community as a whole but also for different campus network stakeholders (e.g., campus IT). Fully leveraging these campus networks' dual role as data source and testbed and seamlessly integrating it into the university's engineering curriculum suggests radically new approaches to teaching, training, and educating engineering students in the era of AI and ML.The project information will be maintained at: https://democratize-netai.cs.ucsb.edu/.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.