题目：《Workload Dependent Dynamic Power Management in Cloud Computing》  

　　　　报告人：李克勤   教授 

　　　　时  间：2017年3月21日15:30-17:30 

　　　　报告地点：电子信息大楼709 

　　　　举办单位：计算机与信息工程学院 

　　　　报告人简历： 

　　　　李克勤，IEEE Fellow, 湖南大学信息科学与工程学院国家“千人计划”特聘教授，博士生导师，超级计算与云计算研究所所长。1985年毕业于清华大学，获计算机科学学士学位；1990年毕业于休斯顿大学，获计算机科学博士学位。之后受聘于纽约州立大学，历任助理教授、副教授、正教授；2009年晋升为纽约州立大学讲席教授；2011年被聘请为清华大学高智讲座教授；2012年受聘为中组部国家千人计划特聘教授。研究领域主要包括算法设计与分析、并行与分布式计算、异构计算系统、高能效计算和通信、物联网和信息物理系统等。他在网格网络的处理器分配和作业调度以及光网络上的并行计算方面做出了许多开创性研究成果。他是 IEEE Trans. on Parallel and Distributed Systems, IEEE Trans. on Computers, IEEE Trans. on Cloud Computing, Journal of Parallel and Distributed Computing 等著名学术刊物的编委。 

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　　    报告内容摘要： 

　　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  onsumption 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.