Mining Software Aging Patterns by Artificial Neural Networks

  • Hisham El-Shishiny
  • Sally Deraz
  • Omar Bahy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5064)


This paper investigates the use of Artificial Neural Networks (ANN) to mine and predict patterns in software aging phenomenon. We analyze resource usage data collected on a typical long-running software system: a web server. A Multi-Layer Perceptron feed forward Artificial Neural Network was trained on an Apache web server dataset to predict future server swap space and physical free memory resource exhaustion through ANN univariate time series forecasting and ANN nonlinear multivariate time series empirical modeling. The results were benchmarked against those obtained from non-parametric statistical techniques, parametric time series models and other empirical modeling techniques reported in the literature.


Data Mining Artificial Neural Network Pattern Recognition Software Aging 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Hisham El-Shishiny
    • 1
  • Sally Deraz
    • 1
  • Omar Bahy
    • 1
  1. 1.IBM Cairo Technology Development CenterGizaEgypt

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