应用托管环境下的虚拟机优化调度方法

J4 ›› 2014, Vol. 36 ›› Issue (01): 44-50.

应用托管环境下的虚拟机优化调度方法

姜永1，范庆涛2，赵卓峰1，丁维龙1

（1.北方工业大学云计算研究中心，北京 100144； 2.泰山索道运营中心，山东泰安 271000）

收稿日期:2013-08-05 修回日期:2013-10-25 出版日期:2014-01-25 发布日期:2014-01-25
基金资助:
北京市自然科学基金重点项目（4131001）；北京市自然科学基金资助项目（4133083）；北京市属高等学校创新团队建设与教师职业发展计划资助项目；北京市产学研联合培养研究生基地（PXM2013_014212_000028）；北方工业大学校科研基金资助项目

An optimization method for virtual machine scheduling in the environment of applications hosting

JIANG Yong1,FAN Qingtao2,ZHAO Zhuofeng1,DING Weilong1

(1.Research Center for Cloud Computing,North China University of Technology,Beijing 100144;
2 Taishan Ropeway Operation Center,Taian 271000,China)

Received:2013-08-05 Revised:2013-10-25 Online:2014-01-25 Published:2014-01-25

摘要/Abstract

摘要：

在面对云服务中典型的应用托管需求时，现有的基础设施即服务IaaS大多采用应用无关的方式进行虚拟机调度，无法针对类型不同的应用托管需求调整调度策略，从而会产生虚拟机集群的负载倾斜及资源利用率不高的情况，甚至会影响所托管的应用。针对上述问题，提出了一种应用托管环境下的虚拟机优化调度方法，通过对所托管应用的分析和物理资源的监控，以贪心方式实现虚拟机周期性的调度策略。实际项目中的应用托管实例表明，该虚拟机调度方法可有效减少所使用的物理服务器数量，并提高物理服务器资源利用率。

关键词: IaaS, 应用托管, 虚拟机调度, 贪心算法

Abstract:

In the environment of applications hosting, the virtual machines of most current IaaS (Infrastructure as a Service) are scheduled in an applicationindependent way and cannot be adjusted for different dynamic requirements, which leads to load skew among virtual machines and inefficient resource utilization for the hosted applications. To solve these problems, an optimal scheduling method for virtual machines is proposed. Through the analysis and the monitoring on the physical resources for the hosted applications, virtual machines can be scheduled periodically in a greedy fashion. In the experiments from our application hosting environment, the method can reduce the number of physical servers and improve the utilization on those physical servers.

Key words: IaaS;application hosting;virtual machine scheduling;greedy algorithm

姜永1，范庆涛2，赵卓峰1，丁维龙1. 应用托管环境下的虚拟机优化调度方法[J]. J4, 2014, 36(01): 44-50.

JIANG Yong1,FAN Qingtao2,ZHAO Zhuofeng1,DING Weilong1. An optimization method for virtual machine scheduling in the environment of applications hosting [J]. J4, 2014, 36(01): 44-50.

参考文献［11］

［1］	Armbrust M, Fox A, Griffith R, et al. Above the clouds: A Berkeley view of cloud computing. EECS Department［R］. Berkeley:University of California, 2009.
［2］	Dodda R, Smith C, Moorsel A. An architecture for crosscloud system management［C］∥Proc of the 2nd International Conference on Contemporary Computing, 2009：556567.
［3］	Barham P, Dragovic B, Fraser K, et al. Xen and the art of virtualization［C］∥Proc of the 19th ACM Symposium on Operating Systems Principles, 2003:164177.
［4］	Nurmi D, Wolski R, Grzegorczyk C, et al. Eucalyptus: A technical report on an elastic utility computing archietcture linking your programs to useful systems［S］. California:UCSBComputer Science Technolog Report Number,200810.
［5］	Li W, Tordsson J, Elmroth E. Virtual machine placement for predictable and timeconstrained peak loads［C］∥Proc of the 8th International Conference on Economics of Grids, Clouds, Systems, and Services, 2012:120134.
［6］	Li W, Tordsson J, Emroth E. Modeling for dynamic cloud scheduling via migration of virtual machines［C］∥Proc of the 3rd IEEE International Conference on Cloud Computing Technology and Science, 2011:163171.
［7］	Li W, Svard P, Tordsson J, et al. A general approach to service deployment in cloud environments［C］∥Proc of the 2nd International Conference on Cloud and Green Computing, 2012:1724.
［8］	Minhas U F, Yadav J, Aboulnaga A, et al. Database systems on virtual machines: How much do you lose? ［C］∥Proc of the 24th IEEE International Conference on Data Engineering Workshop, 2008:3541.
［9］	Maximilien E M, Ranabahu A, Engehausen R, et al. Toward cloudagnostic middlewares［C］∥Proc of the 24th ACM SIGPLAN Conference Companion on Object Oriented Programming Systems Languages and Applications, 2009:619626.
［10］	Kllapi H, Sitaridi E, Tsangaris M M, et al. Schedule optimization for data processing flows on the cloud［C］∥Proc of 2011 ACM SIGMOD International Conference on Management of Data, 2011:289300.
［11］	Hu J, Gu J, Sun G, et al. A scheduling strategy on load balancing of virtual machine resources in cloud computing environment［C］∥Proc of the 3rd International Symposium on Parallel Architectures, Algorithms and Programming, 2010:8996.

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