Research on Cloud Platform Software Aging Prediction Method Based on VMD-ARIMA-BilSTM Combined Model

Abstract

AbstractWhen the cloud platform runs under heavy load for a long time, internal resources will be consumed and errors will accumulate continuously. As a result, the software aging phenomenon occurs, which ultimately degrades the performance and reliability of the software system. Aiming at the above problems, this paper proposes a hybrid model based on integrated variational mode decomposition, moving average free regression and long and short memory network (VMD-ARIMA-BILSTM) to predict the software aging problem. Firstly, the original resource utilization rate is decomposed into stationary time series and non-stationary time series by variational mode decomposition. Then, the advantages of moving average free regression and bidirectional long short-term memory network are used to predict stationary and non-stationary series respectively. Finally, the prediction results are reconstructed to obtain the final prediction results. Experimental results show that compared with single ARIMA and BI-LSTM, the hybrid model designed in this paper has higher prediction accuracy and faster convergence speed.Keywords: Cloud platformsoftware agingVMDARIMABILSTM Disclosure StatementNo potential conflict of interest was reported by the author(s).