虚拟化计算环境中虚拟机启动时间的分析与预测

Ridlo Sayyidina Auliya, Yen-Lin Lee, Chia-Ching Chen, Deron Liang, Wei-Jen Wang
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摘要

启动虚拟机(VM)是云计算平台中的一项常见操作。为了更好地管理资源配置,云计算平台需要准确估计虚拟机的启动时间。在本文中,我们进行了多项实验,分析了在共享存储的计算机集群中可能影响虚拟机启动时间的因素。我们还实现了四种用于预测虚拟机启动时间的模型,并根据环境中四台主机和七台主机的数据集评估了四种模型的性能,其中四种模型分别是基于规则的模型、回归树模型、随机森林回归模型和线性回归模型。根据分析,我们发现主机能力和最大网络带宽是影响虚拟机启动时间的两个主要因素。我们还发现,在不同主机上同时启动虚拟机时,由于主机之间为获取资源而竞争,虚拟机启动时间变得更难预测。实验结果表明,所提出的随机森林回归是预测虚拟机启动时间的最佳模型,在两个分别有 4 台和 7 台计算主机的集群中,预测虚拟机启动时间的平均准确率分别为 94.76 $$\%$ 和 96.59 $$\%$。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis and prediction of virtual machine boot time on virtualized computing environments
Starting a virtual machine (VM) is a common operation in cloud computing platforms. In order to achieve better management of resource provisioning, a cloud platform needs to accurately estimate the VM boot time. In this paper, we have conducted several experiments to analyze the factors that could affect VM boot time in a computer cluster with shared storage. We also implemented four models for VM boot time prediction and evaluated the performance of the four models based on the datasets of four hosts and seven hosts in our environment, where the four models are the rule-based model, the regression tree model, the random forest regression model, and the linear regression model. According to our analysis, we found that host capability and maximal network bandwidth are two main factors that can influence VM boot time. We also found that VM boot time becomes harder to predict when booting VMs at different hosts concurrently due to competition between hosts to obtain resources. According to the experimental results, the proposed random forest regression is the best model for VM boot time prediction with an average accuracy of 94.76 $$\%$$ and 96.59 $$\%$$ in predicting VM boot time in two clusters with four and seven compute hosts, respectively.
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