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| 一种面向虚拟化数字中心资源按需重配置方法 |
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米海波1, 王怀民1, 尹刚1, 史殿习1, 周扬帆2, 袁霖1,3
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1.国防科学技术大学 并行与分布处理国家重点实验室,湖南 长沙 410073;2.香港中文大学 计算机科学与工程学系,香港;3.解放军信息工程大学 电子技术学院,河南 郑州 450004
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| 摘要: |
| 面向Web 应用,提出一种动态资源按需配置方法,能够根据不断变化的资源需求以在线方式高效地重配置集群,实时地确定集群当前节点运行数量及其上部署的虚拟机类型.该方法基于布尔二次指数平滑法预测用户请求,有效避免了配置结果落后于资源请求;基于遗传算法并行化搜索配置空间,快速发现合理配置.实验结果表明,该方法能够根据需求变化高效地在线调整系统资源配置,并可有效提高集群资源利用率,显著降低了系统能耗. |
| 关键词: 数据中心 虚拟化技术 在线重配置 遗传算法 能耗 |
| DOI：10.3724/SP.J.1001.2011.04056 |
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| 基金项目:国家自然科学基金(90818028, 60903043); 国家高技术研究发展计划(863)(2007AA010301); 国家杰出青年科学基金(60625203) |
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| Resource On-Demand Reconfiguration Method for Virtualized Data Centers |
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MI Hai-Bo1, WANG Huai-Min1, YIN Gang1, SHI Dian-Xi1, ZHOU Yang-Fan2, YUAN Lin1,3
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1.National Key Laboratory of Parallel and Distributed Processing, National University of Defense Technology, Changsha 410073, China;2.Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong;3.School of Electronic Technology, Information Engineering University, Zhengzhou 450004, China
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| Abstract: |
| This paper proposes a resource on-demand approach for Web applications, which can efficiently online reconfigure clusters in response to time-varying resource requirements. It can also dynamically decide the number of running nodes and virtual machines deployed on them. It first predicts the future workloads of the applications with Brown’s quadratic exponential smoothing method to make reconfiguration catch up with demands. Next, it adopts a genetic algorithm to parallel find the optimal reconfiguration policy. Experimental results demonstrate the approach can online adapt the cluster resource according to the change of requirement, increase the cluster resource utilization and greatly reduce power consumption. |
| Key words: data center virtualization technology online reconfiguration genetic algorithm power consumption |
