Load prediction optimization based on machine learning in cloud computing environment

Abstract

The load prediction of host resources is a key issue to enhance the cloud computing aid allocation system. With the change of cloud computing resource load displaying extra and extra complicated characteristics, traditional prediction algorithms can solely predict the linear traits of data, and it is tough to precisely predict useful resource usage. In order to enhance the forecasting accuracy of the model, a blended load forecasting algorithm based totally on machine learning is proposed. The machine learning prediction model can nicely match the nonlinear traits of the data. The linear phase of the algorithm makes use of ARIMA prediction, and the nonlinear section makes use of particle swarm optimization algorithm to optimize LSTM prediction. Then, the optimal least squares method is used to redistribute the prediction error weights of the autoregressive differential moving average model (ARIMA) and the long-term and short-term memory network models (LSTM), and finally the prediction results are output. The comparison experiment is carried out with the open actual load data set. The experimental results show that the prediction accuracy of the weight redistribution combination model is significantly higher than that of other traditional prediction models and machine learning prediction models when the prediction time efficiency is similar, and the real-time prediction error of resource load in cloud environment is significantly reduced.