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ESE Fall Seminar – “Learning-NUM: Utility Maximization in Stochastic Queueing Networks”
October 15 at 11:00 AM - 12:00 PM
We consider the problem of network utility maximization (NUM) and propose a new Learning-NUM framework, where the users’ utility functions are unknown apriori and the utility function values can be observed only after the corresponding traffic is delivered to the destination. We start by considering linear utility functions and propose a priority-based network control policy, that combines techniques from network control and multi-arm bandits to achieve logarithmic regret. We then consider the case of concave utility functions and design the Gradient Sampling Max-Weight algorithm (GSMW), based on the ideas of gradient estimation and Max-Weight scheduling, that achieves sublinear utility regret. We further demonstrate the applicability of the gradient sampling approach to minimum delay routing in wireless networks. Finally, we consider the general problem of reinforcement learning for queueing networks with unbounded state-spaces, with the goal of making control decisions that minimizing the queue length. We formulate the problem as an MDP, and propose a new reinforcement learning framework, called Truncated Upper Confidence Reinforcement Learning (TUCRL), that can achieve optimal performance. We show how this framework can be applied to deep reinforcement learning (DRL) for online stochastic network optimization.
Eytan Modiano
The Richard C. Maclaurin Professor of Aeronautics and Astronautics and the Laboratory for Information and Decision Systems (LIDS) , MIT
Eytan Modiano is The Richard C. Maclaurin Professor in the Department of Aeronautics and Astronautics and the Laboratory for Information and Decision Systems (LIDS) at MIT. Prior to Joining the faculty at MIT in 1999, he was a Naval Research Laboratory Fellow between 1987 and 1992, a National Research Council Post Doctoral Fellow during 1992-1993, and a member of the technical staff at MIT Lincoln Laboratory between 1993 and 1999. Eytan Modiano received his B.S. degree in Electrical Engineering and Computer Science from the University of Connecticut at Storrs in 1986 and his M.S. and PhD degrees, both in Electrical Engineering, from the University of Maryland, College Park, MD, in 1989 and 1992 respectively.
His research is on modeling, analysis and design of communication networks and protocols. He received the Infocom Achievement Award (2020) for contributions to the analysis and design of cross-layer resource allocation algorithms for wireless, optical, and satellite networks. He is the co-recipient of the Infocom 2018 Best paper award, the MobiHoc 2018 best paper award, the MobiHoc 2016 best paper award, the Wiopt 2013 best paper award, and the Sigmetrics 2006 best paper award. He was the Editor-in-Chief for IEEE/ACM Transactions on Networking (2017-2020), and served as Associate Editor for IEEE Transactions on Information Theory and IEEE/ACM Transactions on Networking. He was the Technical Program co-chair for IEEE Wiopt 2006, IEEE Infocom 2007, ACM MobiHoc 2007, and DRCN 2015; and general co-chair of Wiopt 2021. He had served on the IEEE Fellows committee in 2014 and 2015, and is a Fellow of the IEEE and an Associate Fellow of the AIAA.