基于ARIMA-RNN组合模型的云服务器老化预测方法

针对云服务器系统运行环境具有非线性、随机性和突发性的特点,提出了基于整合移动平均自回归和循环神经网络组合模型(ARIMA-RNN)的软件老化预测方法。首先,采用ARIMA模型对云服务器时间序列数据进行老化预测;然后,利用灰色关联度分析法计算时间序列数据的相关性,确定RNN模型的输入维度;最后,将ARIMA模型预测值和历史数据作为RNN模型的输入进行二次老化预测,从而克服了ARIMA模型对波动较大的时间序列数据预测精度较低的局限性。实验结果表明,ARIMA-RNN组合模型比ARIMA模型及RNN模型的预测精度高,且比RNN模型预测收敛速度快。

Cloud server aging prediction method based on hybrid model of auto-regressive integrated moving average and recurrent neural network

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.