报告人简介：Minghua Chen received his B.Eng. and M.S. degrees at Tsinghua University in 1999 and 2001. He received his Ph.D. degree at University of California at Berkeley in 2006. He joined the Department of Information Engineering, the Chinese University of Hong Kong, in 2007. He received the Eli Jury award from UC Berkeley in 2007 and The Chinese University of Hong Kong Young Researcher Award in 2013. He also received several best paper awards, including the IEEE Transactions on Multimedia Prize Paper Award in 2009, and the ACM Multimedia Best Paper Award in 2012. In online service systems, the delay experienced by a user from the service request to the service completion is one of the most critical performance metrics. To improve user delay experience, recent industrial practice suggests a modern system design mechanism: proactive serving, where the system predicts future user requests and allocates its capacity to serve these upcoming requests proactively. This approach is complementary to the conventional capability boosting mechanism. In this report, we propose queuing models for online service systems with proactive serving capability and characterize the user delay reduction by proactive serving. In particular, we show that proactive serving decreases average delay exponentially (as a function of the prediction window size) for the case where service time follows light-tailed distribution. Our trace-driven evaluations show that, for YouTube data trace of light-tailed videos, the average user delay decreases by 50% when the system predicts one minute ahead. Our results provide useful insights for proactive serving and justify its increasing applications in practical systems.