Prediction-Based Software Availability Enhancement

  • Felix Salfner
  • Günther Hoffmann
  • Miroslaw Malek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3460)


We propose a new paradigm for software availability enhancement. We offer a two-step strategy: Failure prediction followed by maintenance actions with the objective of avoiding impending failures or minimizing the effort of their repair. For the first step we present two failure prediction methods: universal basis functions (UBF) and similar events prediction (SEP), which are based on probabilistic analysis. The potential of the presented methods is evaluated by a case-study where failures of a commercial telecommunication platform have been predicted. The second step includes existing maintenance methods fitting the proposed approach and a new recovery strategy called “adaptive recovery blocks”. Since system availability enhancement is the overall goal, equations to calculate availability of such a system are given as well.


Preventive Maintenance Software Aging Acceptance Test System Availability Failure Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Sullivan, M., Chillarege, R.: Software defects and their impact on system availability -a study of field failures in operating systems. In: 21st Int. Symp. on Fault-Tolerant Computing (FTCS-21), pp. 2–9 (1991)Google Scholar
  2. 2.
    Garg, S., Puliafito, A., Telek, M., Trivedi, K.S.: Analysis of Preventive Maintenance in Transactions Based Software Systems. IEEE Trans. Comput. 47(1), 96–107 (1998)CrossRefGoogle Scholar
  3. 3.
    Huang, Y., Kintala, C., Kolettis, N., Fulton, N.: Software Rejuvenation: Analysis, Module and Applications. In: Proceedings of IEEE Intl. Symposium on Fault Tolerant Computing, FTCS 25 (1995)Google Scholar
  4. 4.
    Dohi, T., Goseva-Popstojanova, K., Trivedi, K.S.: Statistical Non-Parametric Algorihms to Estimate the Optimal Software Rejuvenation Schedule. In: Proceedings of the Pacific Rim International Symposium on Dependable Computing, PRDC (2000)Google Scholar
  5. 5.
    Garg, S., van Moorsel, A., Vaidyanathan, K., Trivedi, K.S.: A Methodology for Detection and Estimation of Software Aging. In: Proceedings of the Int’l. Symp. on Software Reliability Engineering, ISSRE (1998)Google Scholar
  6. 6.
    Vaidyanathan, K., Trivedi, K.S.: A Measurement-Based Model for Estimation of Resource Exhaustion in Operational Software Systems. In: Proceedings of the International Symposium on Software Reliability Engineering, ISSRE (1999)Google Scholar
  7. 7.
    Li, L., Vaidyanathan, K., Trivedi, K.S.: An Approach for Estimation of Software Aging in a Web Server. In: Proceedings of the Intl. Symposium on Empirical Software Engineering, ISESE (2002)Google Scholar
  8. 8.
    Fox, A., Kiciman, E., Patterson, D., Katz, R., Jordan, M., Stoica, I.: Statistical Monitoring + Predictable Recovery = Self-*. In: Proceedings of the Internation Workshop on Self-* Properties in Complex Information Systems, SELF-STAR (2004)Google Scholar
  9. 9.
    Lin, T., Siewiorek, D.P.: Error log analysis: statistical modeling and heuristic trend analysis. IEEE Transactions on Reliability 39(4), 419–432 (1990)Google Scholar
  10. 10.
    Salfner, F., Tschirpke, S., Malek, M.: Comprehensive Logfiles for Autonomic Systems. In: Proceedings of 9th IEEE Workshop on Fault-Tolerant Parallel, Distributed and Network-Centric Systems (2004)Google Scholar
  11. 11.
    Geman, S., Bienenstock, E., Doursat, R.: Neural Networks and the Bias/Variance Dilemma. Neural computation 4(1), 1–58 (1992)CrossRefGoogle Scholar
  12. 12.
    Weigend, A.S., Gershenfeld, N.A. (eds.): Time Series Prediction, 1st edn. Addison-Wesley, Reading (1994)Google Scholar
  13. 13.
    Hoffmann, G.A., Salfner, F., Malek, M.: Advanced Failure Prediction in Complex Software Systems. Research report 172, Department of Computer Science, Humboldt University, Berlin, Germany (2004), Available at:
  14. 14.
    Hoffmann, G.A.: Adaptive Transfer Functions in Radial Basis Function Networks (RBF). In: International Conference on Computational Science (2004)Google Scholar
  15. 15.
    Schoelkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)Google Scholar
  16. 16.
    Malek, M., Salfner, F., Hoffmann, G.A.: Self-Rejuvenation - an Effective Way to High Availability. In: SELF-STAR: International Workshop on Self-* Properties in Complex Information Systems (2004)Google Scholar
  17. 17.
    Castelli, V., Harper, R.E., Heidelberger, P., Hunter, S.W., Trivedi, K.S., Vaidyanathan, K., Zeggert, W.P.: Proactive management of software aging. IBM Journal of Research and Development 45(2), 311–332 (2001)CrossRefGoogle Scholar
  18. 18.
    Pfening, A., Garg, S., Puliafito, A., Telek, M., Trivedi, K.S.: Optimal Software Rejuvenation for Tolerating Soft Failures. Performance Evaluation 27, 28 (1996)Google Scholar
  19. 19.
    Garg, S., Telek, M., Puliafito, A., Trivedi, K.S.: Analysis of Software Rejuvenation using Markov Regenerative Stochastic Petri Net. In: Proceedings of the International Symposium on Software Reliability Engineering, ISSRE 1995 (1995)Google Scholar
  20. 20.
    Trivedi, K.S., Vaidyanathan, K., Goseva-Popstojanova, K.: Modeling and Analysis of Software Aging and Rejuvenation. In: Proceedings of the IEEE Annual Simulation Symposium (2000)Google Scholar
  21. 21.
    Randell, B.: System structure for software fault tolerance. IEEE Transactions on Software Engineering 1(2), 220–232 (1975)Google Scholar
  22. 22.
    Ferber, R.: Information Retrieval: Suchmodelle und Data-Mining-Verfahren für Textsammlungen und das Web. dpunkt.verlag, Heidelberg (2003)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Felix Salfner
    • 1
  • Günther Hoffmann
    • 1
  • Miroslaw Malek
    • 1
  1. 1.Institut für InformatikHumboldt-Universität zu Berlin 

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