• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

• 论文 • 上一篇    下一篇

云计算中能耗和性能感知的虚拟机优化部署算法

房丙午1,2,黄志球1   

  1. (1.南京航空航天大学计算机科学与技术学院,江苏 南京 211100;2.安徽财贸职业学院电子信息系,安徽 合肥 230601)
  • 收稿日期:2015-10-14 修回日期:2015-12-07 出版日期:2016-12-25 发布日期:2016-12-25
  • 基金资助:

    国家自然科学基金(61272083);安徽省教育厅自然科学基金(KJ2013B009,KJ2013B010)

An energyandperformanceaware virtual machine
placement optimization algorithm in cloud computing
 

FANG Bingwu1,2,HUANG Zhiqiu1   

  1. (1.College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211100;
    2.Department of Electronics and Information,Anhui Vocational College of Finance and Trade,Hefei 230601,China)
  • Received:2015-10-14 Revised:2015-12-07 Online:2016-12-25 Published:2016-12-25

摘要:

优化虚拟机部署是数据中心降低能耗的一个重要方法。目前大多数虚拟机部署算法都明显地降低了能耗,但过度虚拟机整合和迁移引起了系统性能较大的退化。针对该问题,首先构建虚拟机优化部署模型。然后提出一种二阶段迭代启发式算法来求解该模型,第一阶段是基于首次适应下降装箱算法,提出一种虚拟机优化部署算法,目标是最小化主机数;第二阶段是提出了一种虚拟机在线迁移选择算法,目标是最小化待迁移虚拟机数。实验结果表明,该算法能够有效地降低能耗,具有较低的服务等级协定(SLA)违背率和较好的时间性能。

关键词: 云计算, 虚拟机部署, 在线迁移, 能耗和性能感知

Abstract:

Optimizing virtual machine placement is an important way to reduce energy consumption in the data center. At present, most placement algorithms of virtual machine can reduce energy consumption significantly, but a considerable degradation of system performance is caused by excessive migration and consolidation of the virtual machine. To solve this problem, we first build an optimization model of virtual machine placement and then propose a twophase iterative heuristic algorithm to solve the model. The first phase is using the optimization placement algorithm of virtual machine to minimize the number of hosts based on the first fit decreasing binpacking algorithm. The second phase is using the live migration selection algorithm of virtual machine to minimize the number of virtual machine migration. Experimental results show that the proposed algorithms can effectively reduce energy consumption, with lower service level agreement (SLA) violation rate and better time performance.

Key words: cloud computing, virtual machine placement, live migration, energyandperformanceaware