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Performance evaluation and dynamic optimization of speed scaling on web servers in cloud computing

ISSN号：1007-0214
期刊名称：Tsinghua Science and Technology
时间：2013
页码：298-307
分类：TP393[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术] TP301.6[自动化与计算机技术—计算机系统结构;自动化与计算机技术—计算机科学与技术]
作者机构：[1]the Department of Computer Science and Technology, Tsinghua University,Beijing100084,China., [2]the Research Institute of Information Technology, and Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, the College of Information Engineering, Inner Mongolia University of Technology,Jinchuan Development Area, Hohhot, Inner Mongolia 010080, China,Beijing100084,China, [3]the Beijing Municipal Commission of Economy and Information,Beijing100029,China
相关基金：supported by the National Key Basic Research and Development （973） Program （Nos. 2012CB315801, 2011CB302805, 2010CB328105,and 2009CB320504）; the National Natural Science Foundation of China （Nos. 60932003, 61020106002, and 61161140320）; the Intel Research Council with the title of “Security Vulnerability Analysis based on Cloud Platform with Intel IA Architecture”
相关项目：无线自组织网络安全特性基础理论研究

关键词： Web服务器, 计算性能, 优化速度, 绩效评价, 缩放, 随机PETRI网, 马尔可夫决策, 状态空间模型, cloud computing, green IT, energy consumption, data centers, stochastic Petri nets, performance evaluation, dynamic optimization, service computing

中文摘要：

The energy consumption in large-scale data centers is attracting more and more attention today with the increasing data center energy costs making the enhanced performance very expensive. This is becoming a bottleneck to further developments in terms of both scale and performance of cloud computing. Thus, the reduction of the energy consumption by data centers is becoming a key research topic in green IT and green computing. The web servers providing cloud service computing run at various speeds for different scenarios. By shifting among these states using speed scaling, the energy consumption is proportional to the workload, which is termed energy-proportionality. This study uses stochastic service decision nets to investigate energy-efficient speed scaling on web servers. This model combines stochastic Petri nets with Markov decision process models. This enables the model to dynamically optimize the speed scaling strategy and make performance evaluations. The model is graphical and intuitive enough to characterize complicated system behavior and decisions. The model is service-oriented using the typical service patterns to reduce the complex model to a simple model with a smaller state space. Performance and reward equivalent analyse substantially reduces the system behavior sub-net. The model gives the optimal strategy and evaluates performance and energy metrics more concisely.

英文摘要：

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