题目：Operating Online Service Platforms under Uncertainty
主讲人：Xiaojun Lin 普度大学 教授
时间：上午10:30am - 11:30
We study the following online learning and control problem in queueing systems, which is motivated by the operation of online service platforms (such as online ad, crowd-sourcing, online labor market and online rental market). Un-labeled clients arrive according to a stochastic process. Each client brings a random number of tasks. As tasks are assigned to servers, they produce client/server-dependent random payoffs. The system operator wants to assign tasks to servers so that the total expected payoff is maximized. However, both the statistics of the dynamic client population and the client-specific payoff vectors are unknown to the operator. Thus, the operator must design task-assignment policies that integrate adaptive control (of the queueing system) with online learning (of the clients’ payoff vectors). We demonstrate that naïve ways of combining online learning with queue control fail to account for the nontrivial closed-loop interactions between the queueing process and the learning process, which may significantly degrade system performance. We propose a new utility-guided online learning and task assignment algorithm that seamlessly integrates learning with control to achieve low regret compared to an oracle that knows everything in advance.
Xiaojun Lin received his B.S. from Zhongshan University, Guangzhou, China, in 1994, and his M.S. and Ph.D. degrees from Purdue University, West Lafayette, Indiana, in 2000 and 2005, respectively. He is currently a Professor of Electrical and Computer Engineering at Purdue University. Dr. Lin's research interests are in the analysis, control and optimization of large and complex networked systems, including both communication networks and cyber-physical systems. He received the IEEE INFOCOM 2008 best paper award and 2005 best paper of the year award from Journal of Communications and Networks. He received the NSF CAREER award in 2007. He is currently serving as an Area Editor for (Elsevier) Computer Networks journal, and have served as an Associate Editor for IEEE/ACM Transactions on Networking and a Guest Editor for (Elsevier) Ad Hoc Networks journal. Dr. Lin is a Fellow of IEEE.