Abstract: The proliferation of networked devices, systems, and applications that we depend on every day makes managing networks more important than ever. The increasing security, availability, and performance demands of these applications suggest that these increasingly difficult network management problems be solved in real time, across a complex web of interacting protocols and systems. Alas, just as the importance of network management has increased, the network has grown so complex that it is seemingly unmanageable. In this new era, network management requires a fundamentally new approach. Instead of optimizations based on closed-form analysis of individual protocols, network operators need data-driven, machine-learning-based models of end-to-end and application performance based on high-level policy goals and a holistic view of the underlying components. Instead of anomaly detection algorithms that operate on offline analysis of network traces, operators need classification and detection algorithms that can make real-time, closed-loop decisions. Networks should learn to drive themselves. This paper explores this concept, discussing how we might attain this ambitious goal by more closely coupling measurement with real-time control and by relying on learning for inference and prediction about a networked application or system, as opposed to closed-form analysis of individual protocols.

This is part of the Fermilab, Argonne, UChicago Computational Science Series organized by the Joint Task Force Initiative.

Bio: Nick Feamster is a Neubauer Professor of Computer Science and The College and the Director of Center for Data and Computing (CDAC) at the University of Chicago. His research focuses on many aspects of computer networking and networked systems, with a focus on network operations, network security, and censorship-resistant communication systems. He received his Ph.D. in Computer science from MIT in 2005, and his B.S. and M.Eng. degrees in Electrical Engineering and Computer Science from MIT in 2000 and 2001, respectively. He was an early-stage employee at Looksmart (acquired by AltaVista), where he wrote the company’s first web crawler; and at Damballa, where he helped design the company’s first botnet-detection algorithm. Nick is an ACM Fellow and has received the Presidential Early Career Award for Scientists and Engineers (PECASE) for his contributions to cybersecurity, notably spam filtering. His other honors include the Technology Review “35 Top Young Innovators Under 35” award, among many other awards.

Website: https://cdac.uchicago.edu/events/next-generation-high-performance-computing/