Abstract: Today, the internet addresses a broader array of applications and infrastructure constraints than ever. With the broadening requirements, current networks with their statically configured protocols provide suboptimal performance. My research group addresses these challenges by designing systems abstractions that enable rapid updates and integrating a combination of learning algorithms and statistical techniques into our abstractions.
In this talk, I will describe a set of directions that my research group has been focusing on for programmable networks: First, I will discuss SCC, our abstraction for rapid network reconfiguration that introduces causal consistency semantics into network updates. Then, I will introduce P4Visor, our framework for virtualizing the network to support multi-armed bandit style learning. Finally, I will highlight the broader challenges for realizing general end-to-end networks that learn and adapt to changing requirements.
Bio: Theophilus Benson is an Assistant Professor of Computer Science at Brown University. He earned his B.S. from Tufts, Ph.D. from U of Wisconsin - Madison, and post-doctorate from Princeton. Dr. Benson’s research focuses on improving the performance and availability of computer networks. His research was recognized by paper awards, including IMC, EuroSYS, ANRP. Dr. Benson received the NSF CAREER Award, NEC Faculty Award, Google Faculty Award, Facebook Faculty Award (X2), Faculty Research and Engagement Program (X2). https://columbiauniversity.zoom.us/j/99056182570?pwd=RlQ5TlRvWG1GTE8wS2FubkZrNXNxZz09#success