计算机与现代化

• 算法设计与分析 • 上一篇    下一篇

虚拟机资源概率配置的云计算SEFFD算法

  

  1. 1.广东科学技术职业学院,广东珠海519090;2.北京中科红旗软件技术有限公司,北京100086
  • 收稿日期:2016-03-14 出版日期:2016-10-15 发布日期:2016-10-14
  • 作者简介:吴伟美(1981-),女,广东珠海人,广东科学技术职业学院实验师,硕士,研究方向:资源优化配置。
  • 基金资助:
    广东科学技术职业学院重点科研项目(XJZD201202); 广东省高等职业教育教学改革立项项目(201401091); 广东省优秀青年教师培养计划项目(Yq2014187)

A Cloud Computing SEFFD Algorithm for Probability #br# Distribution of Virtual Machine Resource

  1. 1. Guangdong Vocational Institute of Science and Technology, Zhuhai 519090, China;

    2. Beijing Red Flag Software Co., Beijing 100086, China
  • Received:2016-03-14 Online:2016-10-15 Published:2016-10-14

摘要:
摘要:为增强虚拟机资源分配过程性能,有效解决云计算环境下虚拟资源分配的NPhard问题,利用模拟进化算法结合首次下降算法构建虚拟资源分配优化过程(SEFFD)。首先,构建全新的虚拟资源分配的评估方式,并结合模拟进化过程较强的算法寻优爬坡效果,采用迭代方式实现虚拟资源分配过程的个体选择、评估以及排序进化;其次,以模拟进化(SE)过程所获得资源分配结果为基础,结合首次下降(FFD)算法准则,实现物理主机及虚拟机资源的二次分配,从而获得资源分配效果和效率的同步提升;最后,利用CloundSim及Gridbus云计算仿真平台对算法性能进行对比测试,实验结果表明所提策略的内存利用率高于60%,处理器利用率大于55%,可有效减少所需物理主机数量,从而降低能耗。

关键词: 模拟进化, 云计算, 虚拟机, 概率优度, NP难优化

Abstract: Aiming at the problem of NP hard optimization in the process of cloud computing virtual machine resource allocation, a new method based on cloud computing simulated evolutionfirst fit decreasing algorithm is proposed to improve the efficiency of virtual resource allocation optimization. Firstly, the optimal degree evaluation scheme of virtual machine resource allocation is put forward by using of the strong ability of climbing of simulated evolution, and for which the choice of virtual resource allocation, evaluation and sorting process is carried out; Secondly, the first fit decreasing rule was adopted to the sort of virtual machine and physical host resource allocation to improve the efficiency and effectiveness of resource allocation; At last, by comparing the experimental results with the CloundSim Grid Laboratory and Gridbus cloud simulation platform, it shows that the proposed algorithm is more than 55% of CPU usage, memory usage rate can reach more than 60%, which can improve the utilization rate of the host resources, and achieve the purpose of energy saving.

Key words: simulation evolution, cloud computing, virtual machine, probability optimization, NP hard optimization

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