基于数据中心的两阶段虚拟机能效优化部署算法

doi:10.11772/j.issn.1001-9081.2014.11.3222

计算机应用 ›› 2014, Vol. 34 ›› Issue (11): 3222-3226.DOI: 10.11772/j.issn.1001-9081.2014.11.3222

基于数据中心的两阶段虚拟机能效优化部署算法

张小庆¹,贺忠堂²

1. 武汉轻工大学数学与计算机学院, 武汉 430023;
2. 中国科学院云计算中心,广东东莞 523808

收稿日期:2014-05-23 修回日期:2014-07-09 出版日期:2014-11-01 发布日期:2014-12-01
通讯作者: 张小庆
作者简介:张小庆(1984-),男,湖北武汉人,讲师,博士,主要研究方向:云计算;贺忠堂(1971-),男,江苏徐州人,高级工程师,博士,主要研究方向:云计算。
基金资助:
武汉轻工大学引进人才科研启动基金资助项目

Two-phase placement algorithm with energy efficiency optimization for virtual machins bsed on data center

ZHANG Xiaoqing¹,HE Zhongtang²

1. School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan Hubei 430023, China;
2. Cloud Computing Center, Chinese Academy of Sciences, Dongguan Guangdong 523808, China

Received:2014-05-23 Revised:2014-07-09 Online:2014-11-01 Published:2014-12-01
Contact: ZHANG Xiaoqing

摘要/Abstract

摘要：

针对数据中心在虚拟机动态部署过程中的高能耗问题,提出了面向数据中心的两阶段虚拟机能效优化部署算法——DVMP_VMMA。第一阶段为初始部署,提出了动态虚拟机部署(DVMP)算法限定主机最优部署数量,降低了闲置能耗;同时,为了应对负载的动态变化,第二阶段提出迁移约束的虚拟机迁移算法(VMMA)对初始部署方案作进一步优化,这样不仅得到的系统能耗更低,而且还能保证应用服务质量。与满载算法(FL)、基于固定门限值的部署算法(FT),绝对中位差部署算法(MAD)、四分位差部署算法(QD)、迁移周期最优算法(MTM)、最小占用率迁移算法(MIU)进行的比较实验结果表明:DVMP_VMMA不仅考虑了系统能耗优化,使运行时资源利用率更高;而且还可以避免VM频繁迁移完成对性能的提升,其在优化数据中心能耗、SLA违例、VM迁移量的控制及性能损失等指标上均有较好效果,其综合性能优于对比算法。

Abstract:

In view of high energy consumption of dynamic virtual machine placement in data centers, a two-phase placement algorithm with energy efficiency optimization for virtual machines, named DVMP_VMMA, was proposed based on data center. The first phase was the initial placement, a Dynamic Virtual Machine Placement (DVMP) algorithm was presented to limit the optimal number of deployed hosts, which reduced the idle energy consumption. Meanwhile, for responding to the dynamic changes of loads, Virtual Machine Migration Algorithm (VMMA) was put forward to further optimize the initial placement with the migration constraints through the dynamic VM (Virtual Machine) migration in second phase, which not only got lower energy consumption of the system, but also ensured Quality of Service (QoS) of applications. Comparison experiments with FL (Full Load), FT (Fixed Threshold), MAD (Median Absolute Deviation), QD (Quartile Deviation), MTM (Migration Time Minimum) and MIU (Minimum Utilization) were given. The experimental results show that DVMP_VMMA not only considers the energy consumption optimization to increase the resource utilization, but also avoids frequent migration of VMs to improve the performance, it gets good effect in optimization of data center energy consumption, SLA (Service Level Agreement) violation, VM migration quantity and loss of performance, its comprehensive performance is better than compared algorithms.

中图分类号:

TP393.1

张小庆贺忠堂. 基于数据中心的两阶段虚拟机能效优化部署算法[J]. 计算机应用, 2014, 34(11): 3222-3226.

ZHANG Xiaoqing HE Zhongtang. Two-phase placement algorithm with energy efficiency optimization for virtual machins bsed on data center[J]. Journal of Computer Applications, 2014, 34(11): 3222-3226.

参考文献

[1]ARMBRUST M, FOX A, GRIFFITH R, et al. A view of cloud computing[J]. Communications of the ACM, 2010, 53(4):50-58.
[2]XIANG J, DING E. Energy-saving optimization in datacenter based on virtual machine scheduling[J]. Journal of Computer Applications, 2013,33(12):3331-3334.(向洁,丁恩杰.基于虚拟机调度的数据中心节能优化[J].计算机应用,2013,33(12):3331-3334.)
[3]TAN Y, ZENG G, WANG W. Policy of energy optimal management for cloud computing platform with stochastic tasks[J]. Journal of Software, 2012, 23(2):266-278.(谭一鸣, 曾国荪, 王伟. 随机任务在云计算平台中能耗的优化管理方法[J]. 软件学报, 2012, 23(2):266-278.)
[4]YE K, WU Z, JIANG X, et al. Power management of virtualized cloud computing platform[J]. Journal of Computer, 2012, 35(6):1262-1285.(叶可江, 吴朝晖, 姜晓红,等. 虚拟化云计算平台的能耗管理[J]. 计算机学报, 2012, 35(6):1262-1285.)
[5]QIAN Y, YU J, WANG W, et al. Energy saving and load balance strategy in cloud computing[J]. Journal of Computer Applications, 2013,33(12):3326-3330.(钱育蓉,于炯,王卫源,等.云计算环境下软硬件节能和负载均衡策略[J].计算机应用,2013,33(12):3326-3330.)
[6]van NGUYEN H, TRAN F D, MENAUD J M. Performance and power management for cloud infrastructures[C]// Proceedings of the 2010 3rd IEEE International Conference on Cloud Computing. Piscataway: IEEE Press,2010: 329-336.
[7]WANG J, ZENG H, HE T, et al. Virtual machine placement and optimization for data center[J]. Journal of Computer Applications, 2013,33(10):2772-2777.(王加昌,曾辉,何腾蛟,等.面向数据中心的虚拟机部署及优化算法[J].计算机应用,2013,33(10):2772-2777.)
[8]BELOGLAZOV A, BUYYA R. Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers[C]// Proceedings of the 8th International Workshop on Middleware for Grids, Clouds and e-Science. New York: ACM Press,2010:1-6.
[9]FAN X, WEBER W D, BARROSO L A. Power provisioning for a warehouse-sized computer[C]// Proceedings of the 34th Annual International Symposium on Computer Architecture. New York: ACM Press, 2007:13-23.
[10]KUSIC D, KEPHART J O, HANSON J E, et al. Power and performance management of virtualized computing environments via lookahead control[J]. Cluster Computing, 2009, 12(1):1-15.
[11]ZHANG X, YUE Q, HE Z. Dynamic energy-efficient virtual machine placement optimization for virtualized clouds[C]// EITRT 2013: Proceedings of the 2013 International Conference on Electrical and Information Technologies for Rail Transportation, LNEE 288. Berlin: Springer-Verlag, 2014:339-448.
[12]CALHEIROS R N, RANJAN R, BELOGLAZOV A, et al. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms[J]. Software-Practice and Experience, 2011, 41(1):23-50.
[13]BELOGLAZOV A, BUYYA R. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers[J]. Concurrency and Computation: Practice and Experience, 2012, 24(13):1397-1420.

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基于数据中心的两阶段虚拟机能效优化部署算法

Two-phase placement algorithm with energy efficiency optimization for virtual machins bsed on data center

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