Arizona State University Network Science Seminar Series

Upcoming Seminar: Learning + Queueing: Operating Online Service Platforms under Uncertainty

Speaker Prof. Xiaojun Lin (Purdue University)
Date 1:00 p.m., Nov 5th, 2018
Location GWC 487
Short Bio
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.
Abstract
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.

Upcoming Seminar: Collaborative Mobile Charging: From Abstraction to Solution

Speaker Jie Wu (Temple University)
Date 3:00 p.m., Nov 16th, 2018
Location CPCOM 103
Short Bio
Jie Wu is the Director of the Center for Networked Computing and Laura H. Carnell professor at Temple University. He also serves as the Director of International Affairs at College of Science and Technology. He served as Chair of Department of Computer and Information Sciences from the summer of 2009 to the summer of 2016 and Associate Vice Provost for International Affairs from the fall of 2015 to the summer of 2017. Prior to joining Temple University, he was a program director at the National Science Foundation and was a distinguished professor at Florida Atlantic University. His current research interests include mobile computing and wireless networks, routing protocols, cloud and green computing, network trust and security, and social network applications. Dr. Wu regularly publishes in scholarly journals, conference proceedings, and books. He serves on several editorial boards, including IEEE Transactions on Mobile Computing, IEEE Transactions on Service Computing, Journal of Parallel and Distributed Computing, and Journal of Computer Science and Technology. Dr. Wu was general co-chair for IEEE MASS 2006, IEEE IPDPS 2008, IEEE ICDCS 2013, ACM MobiHoc 2014, ICPP 2016, and IEEE CNS 2016, as well as program co-chair for IEEE INFOCOM 2011 and CCF CNCC 2013. He was an IEEE Computer Society Distinguished Visitor, ACM Distinguished Speaker, and chair for the IEEE Technical Committee on Distributed Processing (TCDP). Dr. Wu is a CCF Distinguished Speaker and a Fellow of the IEEE. He is the recipient of the 2011 China Computer Federation (CCF) Overseas Outstanding Achievement Award.
Abstract
Wireless energy charging using mobile vehicles has been a viable research topic recently in the area of wireless networks and mobile computing. This talk gives a short survey of recent research conducted in our research group in the area of collaborative mobile charging. In collaborative mobile charging, multiple mobile chargers work together to accomplish a given set of objectives. These objectives include charging sensors at different frequencies with a minimum number of mobile chargers and reaching the farthest sensor for a given set of mobile chargers, subject to various constraints, including speed and energy limits of mobile chargers. Through the process of problem formulation, solution construction, and future work extension for problems related to collaborative mobile charging and coverage, we present three principles for good practice in conducting research, that is, select a simple problem, find an elegant solution, and use imagination for extension.

Seminars

Title Speaker Time Location
Decentralized multi-agent coordination via event-triggered control Zhi Tian (George Mason University) 1:30 p.m., Oct 2rd, 2018 GWC 487
CLearning + Queueing: Operating Online Service Platforms under Uncertainty Xiaojun Lin (Purdue University) 1:00 p.m., Nov 5th, 2018 GWC 487
Collaborative Mobile Charging: From Abstraction to Solution? Jie Wu (Temple University) 3:00 p.m., Nov 16th, 2018 CPCOM 103
 
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