Application server aging prediction model based on wavelet network with adaptive particle swarm optimization algorithm

Abstract

According to the characteristic of performance parameters of application sever, a new software aging prediction model based on wavelet network is proposed. The dimensionality of input variables is reduced by principal component analysis, and the parameters of wavelet network are optimized with adaptive particle swarm optimization (PSO) algorithm. The objective is to observe and model the existing systematic parameter data series of application server to predict accurately future unknown data values. By the model, we can get the aging threshold before application server fails and rejuvenate the application server in autonomic ways before observed 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 adaptive PSO algorithm is effective and more accurate than wavelet network model with Genetic algorithm (GA). © Springer-Verlag Berlin Heidelberg 2007.