Performance Evaluation and Dynamic Optimization of Speed Scaling on Web Servers in Cloud Computing

作     者：Yuan Tian Chuang Lin Zhen Chen Jianxiong Wan Xuehai Peng 

作者机构：the Department of Computer Science and Technology Tsinghua University 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 TechnologyJinchuan Development Area Hohhot Inner Mongolia 010080 China the Beijing Municipal Commission of Economy and Information 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2013年第18卷第3期

页      面：298-307页

核心收录：

学科分类：08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 

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" 

主　　题：cloud computing green IT energy consumption data centers stochastic Petri nets performance evaluation dynamic optimization service computing 

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.