计算机应用 ›› 2014, Vol. 34 ›› Issue (8): 2252-2255.DOI: 10.11772/j.issn.1001-9081.2014.08.2252

• 先进计算 • 上一篇    下一篇

基于灰色关联度的云计算虚拟机分配方法

何丽   

  1. 天津财经大学 信息科学与技术系,天津300222
  • 收稿日期:2014-02-28 修回日期:2014-04-15 出版日期:2014-08-01 发布日期:2014-08-10
  • 通讯作者: 何丽
  • 作者简介:何丽(1969-),女,安徽舒城人,副教授,博士,CCF会员,主要研究方向:Web服务、数据挖掘、云计算。
  • 基金资助:

    天津市高等学校科技发展基金资助项目

Virtual machine allocation method based on gray correlation degree in cloud computing

HE Li   

  1. Department of Information Science and Technology, Tianjin University of Finance and Economics, Tianjin 300222, China
  • Received:2014-02-28 Revised:2014-04-15 Online:2014-08-01 Published:2014-08-10
  • Contact: HE Li
  • Supported by:

    the Science and Technology Development Fund of Tianjin Higher Education Institutions

摘要:

针对云计算系统中资源利用率提高和系统能耗降低之间的协调问题,提出了一种新的基于灰色关联度的虚拟机分配方法,应用灰色关联度的基本理论建立了基于服务层协议(SLA)违背率、系统能耗和服务器负载评价函数的虚拟机分配决策模型,构造了基于灰色关联度的虚拟机分配算法,并在CloudSim仿真平台上进行了实验。实验结果表明,与传统的基于简单线性权重的多目标优化方法相比,在不同的虚拟机选择策略下,基于灰色关联度的虚拟机分配方法在系统能耗、SLA违背率和虚拟机迁移次数上平均降低〖BP(〗是提高吗?应该是降低吧?请明确一下。〖BP)〗了6.8%、5.2%和15.5%。因此,所提方法在不同的虚拟机选择策略下能够大幅度减少虚拟机迁移次数,较好地满足系统在能耗和SLA违背率上的优化需求。

Abstract:

For the balancing problem of improving resource utilization and reducing energy consumption in cloud computing system, a new virtual machine allocation method based on gray correlation degree was proposed. By using the basic theory of gray correlation, the author established an allocation model of virtual machines based on the evaluation functions of Service Level Agreement (SLA) violation rate, system energy consumption and server load, constructed the virtual machine allocation algorithm based on gray correlation degree, and experimented on the platform of CloudSim. The experimental results show that, compared with the traditional multi-objective optimization method based on the simple linear weight, the virtual machine allocation method based on gray correlation degree can achieve average decrease by 6.8%, 5.2% and 15.5% in the system energy consumption, the SLA violation rate and the migrating number of virtual machines under different virtual machine selection strategies. Thus, the proposed method under different virtual machine selection strategies can greatly reduce the migrating number of virtual machines, and meets the demand of system optimization on the energy consumption and SLA violation rate preferably.

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