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| 基于M/M/n/n+r排队模型的云计算中心服务性能分析 | |
| 引用本文： | 何怀文 傅瑜 杨毅红 肖涛. 基于M/M/n/n+r排队模型的云计算中心服务性能分析[J]. 计算机应用, 2014, 34(7): 1843-1847. DOI: 10.11772/j.issn.1001-9081.2014.07.1843 |
| 作者姓名： | 何怀文 傅瑜 杨毅红 肖涛 |
| 作者单位： | 1. 电子科技大学中山学院 计算机学院, 广东 中山 5284022. 中山大学 信息科学与技术学院, 广州 510275;3. 北京理工大学 珠海学院，广东 珠海 519088 |
| 基金项目： | 河北省自然科学基金资助项目;广东省自然科学基金资助项目;中山市科技计划项目 |
| 摘 要： | 针对需要精确地评估分析云数据中心服务性能以保证服务质量(QoS)和避免违反服务水平协议(SLA)的问题，提出了一个基于M/M/n/n+r排队系统云计算中心近似分析模型。通过求解该模型获得用户请求响应时间的分布函数以及其他重要的QoS性能指标，同时通过仿真实验验证和获得服务器数量、队列缓冲区大小与响应时间、请求阻塞概率以及请求立即服务概率之间的关系。实验结果表明，提高服务器服务速率比增加服务器数量更利于提高服务性能。 |
| 关 键 词： | 云计算中心 响应时间 排队模型 服务性能分析 |
| 收稿时间： | 2014-01-15 |
| 修稿时间： | 2014-03-09 |
Service performance analysis of cloud computing center based on M/M/n/n+r queuing model |
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| HE Huaiwen FU Yu YANG Yihong XIAO Tao. Service performance analysis of cloud computing center based on M/M/n/n+r queuing model[J]. Journal of Computer Applications, 2014, 34(7): 1843-1847. DOI: 10.11772/j.issn.1001-9081.2014.07.1843 | |
| Authors: | HE Huaiwen FU Yu YANG Yihong XIAO Tao |
| Affiliation: | 1. School of Computer, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan Guangdong 528402, China; 2. School of Information Science and Technology, Sun Yat-sen University, Guangzhou Guangdong 510275, China; 3. Beijing Institute of Technology, ZHUHAI, Zhuhai Guangdong 519088, China |
| Abstract: | Since it is necessary to evaluate and analyze the service performance of cloud computing center to guarantee Quality of Service (QoS) and avoid violation of Service Layer Agreement (SLA), a approximated analysis model based on M/M/n/n+r queue theory was proposed for cloud computing center. By solving this model the probability distribution function of response time and other QoS indicators were acquired, meanwhile the relationship among the number of servers, size of queue buffers, response time, blocking probability and instance service probability were revealed and verified by simulation.The experimental results indicate that improving server service rate is better than increasing the number of servers for improving service performance. |
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