Flow optimization in the presence of uncertainty about future traffic demands is a fact of life in many operational environments. A common approach for addressing this is predicting future traffic demands and optimizing with respect to these. This, however, can give rise to poor quality solutions and prohibitively expensive runtimes. We present an alternative approach to this fundamental challenge: direct stochastic optimization. We show, through theoretical analyses and extensive empirical evaluation on real-world traffic, that our approach yields both superior quality solutions and significantly faster runtimes.

Based on joint work with Yarin Perry, Felipe Vieira Frujeri, Chaim Hoch, Srikanth Kandula, Ishai Menache, and Aviv Tamar.

Awarded Best Paper at NSDI 2023

About Michael Schapira

Michael Schapira is professor of Computer Science at Hebrew University. His current research interests lie at the interface of networking and machine learning. Prior to joining Hebrew U, he was a visiting scientist at Google NYC’s Infrastructure Networking Group and a postdoctoral researcher at UC Berkeley, Yale University, and Princeton University. He is a recipient of the Wolf Foundation’s Krill Prize, faculty awards from Microsoft, Google, and Facebook, IETF/IRTF Applied Networking Research Prizes, and the IEEE Communications Society William R. Bennett Prize.

Este evento se impartirá en inglés