Cloud server aging prediction method based on hybrid model of auto-regressive integrated moving average and recurrent neural network - Haining MENG; Xinyu TONG; Yuekai SHI; Lei ZHU; Kai FENG; Xinhong HEI (Tongxin xuebao)
In view of the nonlinear, stochastic and sudden characteristics of operating environment of cloud server system, a software aging prediction method based on hybrid auto-regressive integrated moving average and recurrent neural network model (ARIMA-RNN) was proposed.Firstly, the ARIMA model performs software aging prediction of time series data in cloud server.Then the grey relation analysis method was used to calculate the correlation of the time series data to determine the input dimension of RNN model.Finally, the predicted value of ARIMA model and historical data were used as the input of RNN model for secondary aging prediction, which overcomes the limitation that ARIMA model has low prediction accuracy for time series data with large fluctuation.The experimental results show that the proposed ARIMA-RNN model has higher prediction accuracy than ARIMA model and RNN model, and has faster prediction convergence speed than RNN model.