Study on Application Server Aging Prediction Based on Wavelet Network with Hybrid Genetic Algorithm

Hai Ning Meng, Yong Qi, Di Hou, Liang Liu, Hui He

Abstract: Software aging is an important factor that affects the software reliability. According to the characteristic of performance parameters of application sever middleware, a new model for software aging prediction based on wavelet networks is proposed. The structure and parameters of wavelet network are optimized by hybridization of genetic algorithm and simulated annealing algorithm. The objective is to observe and model the existing resource usage time series of application server middleware to predict accurately future unknown resource usage value. Judging by the model, we can get the aging threshold before application server fails and rejuvenate the application server before systematic parameter value reaches the threshold. The experiments are carried out to validate the efficiency of the proposed model, and show that the aging prediction model based on wavelet network with hybrid genetic algorithm is superior to the neural network model and wavelet network model in the aspects of convergence rate and prediction precision.