中山大学数据科学与计算机学院/广东省大数据分析与处理重点实验室

学术报告Research Seminar

------------------------------------------------------------------------------------------------------------------------
Date:  Jan. 06, 2017 (Fri.) | Time: 2:30pm - 3:30pm | Venue: Room A101

------------------------------------------------------------------------------------------------------------------------
Title: Workload Dependent Dynamic Power Management in Cloud Computing

Speaker: Keqin Li, IEEE Fellow，纽约州立大学特聘教授、“****”教授（湖南大学）

Host:  吴维刚，020-39943093  wuweig@mail.sysu.edu.cn

------------------------------------------------------------------------------------------------------------------------
Abstract:

The technique of using workload dependent dynamic power management (i.e., variable power and speed of processor cores according to the current workload) to improve system performance and to reduce energy consumption is investigated. Typically, the power supply and the core speed are increased when there are more tasks in a server, such that tasks can be processed faster and the average task response time is reduced. On the other hand, the power supply and the core speed are decreased when there are less tasks in a server, such that energy consumption can be reduced without significant performance degradation. A queueing model of multicore server processors with workload dependent dynamic power management is established. Several speed schemes are proposed and it is demonstrated that for the same average power consumption, it is possible to design a multicore server processor with workload dependent dynamic power management, such that its average task response time is shorter than a multicore server processor of constant speed (i.e., without workload dependent dynamic power management). It is shown that given certain application environment and average power consumption, there is an optimal speed scheme that minimizes the average task response time. For two-speed schemes, the problem of optimal design of a two-speed scheme for given power supply and power consumption model is formulated and solved. It is pointed out that power consumption reduction subject to performance constraints can be studied in a similar way as performance improvement (i.e., average task response time reduction) subject to power consumption constraints. To the best of our knowledge, this is the first work on analytical study of workload dependent dynamic power management.

-----------------------------------------------------------------------------------------------------------------------
The Speaker:

Dr. Keqin Li is a SUNY Distinguished Professor of computer science in the State University of New York. He is also a Distinguished Professor of Chinese National Recruitment Program of Global Experts (1000 Plan) at Hunan University, China. He was an Intellectual Ventures endowed visiting chair professor at the National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China, during 2011-2014. His current research interests include parallel computing and high-performance computing, distributed computing, energy-efficient computing and communication, heterogeneous computing systems, cloud computing, big data computing, CPU-GPU hybrid and cooperative computing, multicore computing, storage and file systems, wireless communication networks, sensor networks, peer-to-peer file sharing systems, mobile computing, service computing, Internet of things and cyber-physical systems. He has published over 460 journal articles, book chapters, and refereed conference papers, and has received several best paper awards. He is currently or has served on the editorial boards of IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Computers, IEEE Transactions on Cloud Computing, IEEE Transactions on Services Computing, and IEEE Transactions on Sustainable Computing. He is an IEEE Fellow.