基于云计算SME-FFD算法的概率优度虚拟机资源配置  

Probabilistic Goodness Virtual Machine Resource Allocation Based on Cloud Computing SME-FFD Algorithm

作　　者：孙立新[1] 张栩之[1] 吕海洋[1] 

机构地区：[1]烟台大学工学院,山东烟台265713

出　　处：《计算机工程》2016年第5期23-29,共7页Computer Engineering

基　　金：山东省科技计划基金资助项目(13210702D;13210130)

摘　　要：针对云计算虚拟机资源配置过程中存在的NP难问题,提出一种基于云计算SME-FFD的概率优度虚拟机资源配置算法。给出虚拟机资源配置的优度评价方案,利用模拟进化算法较强的爬坡优化能力,对虚拟资源配置的选择、评价和排序过程进行迭代进化。在模拟进化操作获取资源配置排序基础上,利用首次适应下降规则,对已排序的虚拟机和物理主机资源进行二次配置,提高资源配置效率和效果。在墨尔本大学CloundSim网格实验室以及Gridbus云仿真平台上进行实验对比,结果表明,该算法CPU使用率与内存使用率分别达到55%和60%以上,能够有效降低物理机器使用数量,实现节约能耗的目的。Aiming at the problem of NP hard optimization in the process of cloud computing Virtual Machine（VM） resource allocation, a new algorithm based on cloud computing Simulated Evolution-First Fit Decreasing （SME-FFD） is proposed. The optimal degree evaluation scheme of virtual machine resource allocation is put forward by use 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. The FFD rule is adopted to the sort of virtual machine and physical host resource allocation to improve the efficiency and effectiveness of resource allocation. 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.

关 键 词：云计算 模拟进化 概率优度 虚拟机 NP难问题 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]