Abstract: This paper presented a method of nonlinear autoregressive models with exogenous inputs to detect the aging phenomenon of the software system. It figures out the problem existing in current software aging methods that there is no cosideration of the correlation between multivariate and the impact of delay from historical data. We first collected the performance data of the HelixServer-VOD server, did principal component analysis of the data, determined the input dimension, determined the best model order according to AIC criteria, selected the reasonable network model structure eventually. We used the known unaging state samples to train the MARX network in order to establish the identification model of the system, then hypothesis tested the residual of the NARX identification model through the method of sequential probability ratio test, finally judge the aging condition of the system. The experimental result shows that NARX model-based fault detection method can be effectively applied in checking software aging.

Key words: Software aging,Nonlinear autoregressive models with exogenous inputs,HelixServer,Sectuential probability ratio test