A neural-wavelet based methodology for software aging forecasting

A number of recent studies have reported the phenomenon of "software aging", characterized by progressive performance degradation and a sudden crash/hang of a software system due to exhaustion of operating system resources, fragmentation and accumulation of errors. To counteract this phenomenon, this paper proposed a novel four-stage method for software aging forecast in operation. The prior data of software performance parameters are treated as time series. The forecast method combines wavelet multiresolution decomposition and neural networks. First, we apply a smoothing unit based on the wavelet multiresolution analysis to reduce the influence of noise. Second, the special performance data is decomposed into different scales by nondecimated Haar wavelet decomposition. Third, each scale is predicted by a separate neural network. Lastly, the next sample of the original time series is predicted by another neural network. The proposed method is tested using the performance parameters data collected from a realistic software system to evaluate the forecasting performance.