Advertisement

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)

Abstract

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.

Keywords

Data Mining Artificial Neural Network Pattern Recognition Software Aging 

References

  1. 1.
    Bolch, G., Greiner, S., de Meer, H., Trivedi, K.S.: Queueing networks and Markov chains: modeling and performance evaluation with computer science applications. Wiley-Interscience, New York (1998)zbMATHGoogle Scholar
  2. 2.
    Chakraborty, K., Mehrota, K., Mohan Chilukuri, K., Ranka, S.: Forecasting the behaviour of multivariate time series using neural networks. Neural Networks 5, 961–970 (1992)CrossRefGoogle Scholar
  3. 3.
    Chen, M.Y., Kiciman, E., Fratkin, E., Fox, A., Brewer, E.: Pinpoint: Problem determination in large, dynamic internet services. In: DSN 2002: Proceedings of the 2002 International Conference on Dependable Systems and Networks, Washington, DC, USA, pp. 595–604. IEEE Computer Society, Los Alamitos (2002)CrossRefGoogle Scholar
  4. 4.
    Dohi, T., Goseva-Popstojanova, K., Trivedi, K.S.: Analysis of software cost models with rejuvenation. hase, 00:25 (2000)Google Scholar
  5. 5.
    Grottke, M., Li, L., Vaidyanathan, K., Trivedi, K.S.: Analysis of software aging in a web server. IEEE Transactions on Reliability 55(3), 411–420 (2006)CrossRefGoogle Scholar
  6. 6.
    Hassoun, M.H.: Fundamentals of Artificial Neural Networks. MIT Press, Cambridge (1995)zbMATHGoogle Scholar
  7. 7.
    Hoffmann, G.A., Trivedi, K.S., Malek, M.: A best practice guide to resource forecasting for computing systems. IEEE Transactions on Reliability, 615–628 (2007)Google Scholar
  8. 8.
    Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)CrossRefGoogle Scholar
  9. 9.
    Kolettis, N., Fulton, N.D.: Software rejuvenation: Analysis, module and applications. In: FTCS 1995: Proceedings of the Twenty-Fifth International Symposium on Fault-Tolerant Computing, Washington, DC, USA, p. 381. IEEE Computer Society, Los Alamitos (1995)Google Scholar
  10. 10.
    Ning, M.H., Yong, Q., Di, H., Ying, C., Zhong, Z.J.: Software aging prediction model based on fuzzy wavelet network with adaptive genetic algorithm. In: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence, pp. 659–666 (2006)Google Scholar
  11. 11.
    Siegelmann, H., Sontag Eduardo, D.: Neural nets are universal computing devices. Technical Report SYSCON-91-08, Rugters Center for Systems and Control (1991)Google Scholar
  12. 12.
    Xu, J., You, J., Zhang, K.: A neural-wavelet based methodology for software aging forecasting. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 59–63 (2005)Google Scholar
  13. 13.
    Zhang, G.P., Qi, M.: Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 501–514 (2005)Google Scholar

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

Personalised recommendations