关键词: 神经网络, ARMA模型, 负载预测, 小波, 服务器负载

Abstract: The prediction accuracy of server load was improved with a novel prediction method based on wavelet. The non-stationary load series was decomposed and reconstructed into one low frequency signal and several high frequency signals by wavelet. Then the approximate stationary low frequency signal was predicted using ARMA model; the high frequency signals were forecasted respectively using neural networks that had different parameters. After one-step-ahead prediction, the predicted results of these signals were combined into the final predicted result of the original load series. Experiments results show that this method can predict non-stationary server load series efficiently and has higher prediction accuracy than traditional methods.

Key words: Neural network, ARMA model, Load prediction, Wavelet, Server load