基于随机森林的虚拟机性能预测与配置优化

Performance Prediction and Configuration Optimization of Virtual Machines Based on Random Forest

在目前的IaaS云计算服务中,用户可租用不同资源配置的虚拟机,然而用户很难根据资源配置准确估计虚拟机的性能,从而较难根据待部署的应用的性能需求选择恰当配置的虚拟机,这种使用方式使得云主机的资源未得到最充分的利用。因此,文中提出基于随机森林回归模型预测特定配置的虚拟机性能,并在此基础上,根据性能需求,利用遗传算法求解较优的符合性能需求的虚拟机配置,用随机森林性能模型获取种群中各个体的性能预测值以选出最接近性能需求的个体进行交叉操作。实验结果表明,随机森林回归模型能准确预测特定配置的虚拟机的性能,利用遗传算法搜索得出的虚拟机配置的实测性能与性能需求非常接近,并且该算法可以在较短时间内达到收敛。

In IaaS cloud computing,users rent one or more virtual machines with different resource configurations.However,it is difficult for users to accurately estimate the performance of the virtual machine according to the resources allocated.Thus it is hard for them to select an appropriate virtual machine according to the performance requirement of the applications.Therefore,this paper proposed to predict performance of the virtual machine according to their resources and configurations based on random forest.Further,it proposed to use genetic algorithm to search the optimal configuration of the virtual machine which can meet the performance requirement.The difference of the prediction result and the target performance are used as the fitness function.The experimental results show that the random forest model can accurately predict performance of the virtual machine.And the actual performance of the virtual machine configured according to the configuration obtained by the genetic algorithm is very close to the performance requirement,and the convergence can be achieved in a short time.