Abstract: 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 predicterrors 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.

Key words: ARMA, Cloud computing, ENN, Workload forecasting