一种云环境下的主机负载预测方法

Workload Forecasting Method in Cloud

云计算是一种基于信息网络的计算模式和服务模式,它将信息技术资源以服务方式动态、弹性地提供给用户,使用户可以按需使用。由于受到主机的启动时间、资源分配时间以及任务调度时间等因素的影响,在云环境下提供给用户的服务存在时延问题。因此,工作负载预测是云环境下一种重要的能源优化的方式。此外,由于云中工作负载的变化具有十分大的波动性,因此增加了预测模型的预测难度。提出了一种基于自回归模型和Elman神经网络的预测模型(Hybrid Auto Regressive Moving Average model and Elman neural network,HARMA-E),其使用ARMA模型进行预测,再使用ENN模型对ARMA模型的误差进行预测,通过修正ARMA的输出值得到最终的预测值。仿真实验结果表明,该预测模型能够较好地提升主机负载预测值的准确度。

Cloud computing is a model of computing and service based on information network,it provides information technology resource for users in a dynamic and flexible way and the users can use them on demand.Due to the startup time of the host,resource allocation time,task scheduling time and other factors,there is a delay problem in the service providing for user in the cloud environment.Therefore,workload prediction is an important way of energy optimization in cloud environment.In addition,due to the great fluctuation of cloud workload,the prediction difficulty of the model is increased.This paper presented a prediction model（Hybrid Auto Regressive Moving Average model and Elman neural network,HARMA-E）based on autoregressive modal and Elman neural network.Firstly,it uses ARMA model to predict,and then it uses ENN model to predict errors of ARMA model,and the final prediction value is obtained by modifying the input value of ARMA.Experimental results show that the proposed method can effectively improve the prediction accuracy of the host workload.