Software System Rejuvenation Modeling Based on Sequential Inspection Periods and State Multi-control Limits

  • Weichao DangEmail author
  • Jianchao Zeng
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 728)


This paper addresses the issue of software rejuvenation modeling. Rejuvenation strategies with sequential inspection periods and state multi-control limits are proposed here because the inspection-based approach involves the sampling of longer fixed periods of the state of system, which increases the probability of soft failure. The degradation process of the software system interferes with inspection and rejuvenation is modeled as a Markov chain. The steady-state probability density function of the system is thus derived, and a numerical solution of the function is provided. Expressions for mean unavailability time are derived during the inspection period when soft failure occurs. Finally, the steady-state availability of the system is modeled, and the solution to it is obtained using a genetic algorithm. The effectiveness of the model was verified by numerical experiments. Compared with rejuvenation strategies with fixed inspection periods, those with sequential inspection periods yielded greater steady-state availability of the software system.



This research was supported in part by the Chinese National Natural Science Foundation under Grant No. 61573250, the Key Research and Development Program of Shanxi Province under Grant No. 201703D121042-1, the Key Science and Technology Program of Shanxi Province under Grant No. 20130321006-01, the Youth Foundation of Shanxi Province under Grant No. 201601D021065 and the PhD Research Startup Foundation of TYUST under Grant No. 20152021.


  1. 1.
    Gray, J., Siewiorek, D.P.: High-availability computer systems. Computer 24(9), 39–48 (1991)CrossRefGoogle Scholar
  2. 2.
    Sullivan, M., Chillarege, R.: Software defects and their impact on system availability-a study of field failures in operating systems. In: Proceedings of the Twenty-First International Symposium on Fault-Tolerant Computing, FTCS-21 (1991)Google Scholar
  3. 3.
    Huang, Y., Kintala, C., Kolettis, N., Fulton, N.D.: Software rejuvenation: analysis, module and applications. In: Proceedings of 25 International Symposium, Fault-Tolerant Computing, FTCS-25 (1995)Google Scholar
  4. 4.
    Garg, S., Puliafito, A., Telek, M., Trivedi, T.: Analysis of preventive maintenance in transactions based software systems. IEEE Trans. Comput. 47(1), 96–107 (1998)CrossRefGoogle Scholar
  5. 5.
    Cassidy, K.J., Gross, K.C., Malekpour, A.: Advanced pattern recognition for detection of complex software aging phenomena in online transaction processing servers. In: Proceedings of International Conference on Dependable Systems and Networks, DSN 2002 (2002)Google Scholar
  6. 6.
    Grottke, M., Li, L., Vaidyanathan, K., Trivedi, K.S.: Analysis of software aging in a web server. Discuss. Pap. 55(3), 411–420 (2005)Google Scholar
  7. 7.
    Cotroneo, D., Orlando, S., Pietrantuono, R., Russo, S.: A measurement-based ageing analysis of the JVM. Softw. Testing Verif.Reliab. 23(3), 199–239 (2013)CrossRefGoogle Scholar
  8. 8.
    Tai, A.T., Alkalai, L., Chau, S.N.: On-board preventive maintenance: a design-oriented analytic study for long-life applications. Perform. Eval. 35(3–4), 215–232 (1999)CrossRefzbMATHGoogle Scholar
  9. 9.
    Matias, R., Andrzejak, A., Machida, F., Elias, D.: A systematic differential analysis for fast and robust detection of software aging. In: 2014 IEEE 33rd International Symposium on Reliable Distributed Systems (2014)Google Scholar
  10. 10.
    Araujo, J., Matos, R., Alves, V., et al.: Software aging in the eucalyptus cloud computing infrastructure: characterization and rejuvenation. ACM J. Emerg. Technol. Comput. Syst. 636(8), 1557–1564 (2014)Google Scholar
  11. 11.
    Marshall, E.: Fatal error: how patriot overlooked a scud. Science 255(5050), 1347 (1992)CrossRefGoogle Scholar
  12. 12.
    Cotroneo, D., Pietrantuono, R., Russo, S., Trivedi, K.: How do bugs surface? A comprehensive study on the characteristics of software bugs manifestation. J. Syst. Softw. 113, 27–43 (2016)CrossRefGoogle Scholar
  13. 13.
    Cotroneo, D., Natella, R., Pietrantuono, R., et al.: A survey of software aging and rejuvenation studies. ACM J. Emerg. Technol. Comput. Syst. 10(1), 104 (2014)CrossRefGoogle Scholar
  14. 14.
    Bao, Y., Sun, X., Trivedi, K.S.: A workload-based analysis of software aging, and rejuvenation. IEEE Trans. Reliab. 54(3), 541–548 (2005)CrossRefGoogle Scholar
  15. 15.
    Zhao, T.H., Yong, Q.I., Shen, J.Y., et al.: Application server multi-state aging model and optimal rejuvenation strategy research. J. Syst. Simul. 19(8), 1705–1709 (2007)Google Scholar
  16. 16.
    Rinsaka, K., Dohi, T.: Toward high assurance software systems with adaptive fault management. Softw. Qual. J. 24(1), 1–21 (2016)CrossRefGoogle Scholar
  17. 17.
    Wang, D., Xie, W., Trivedi, K.S.: Performability analysis of clustered systems with rejuvenation under varying workload. Perform. Eval. 64(3), 247–265 (2007)CrossRefGoogle Scholar
  18. 18.
    Bobbio, A., Sereno, M., Anglano, C.: Fine grained software degradation models for optimal rejuvenation policies. Perform. Eval. 46(1), 45–62 (2001)CrossRefzbMATHGoogle Scholar
  19. 19.
    Garg, S., Van Moorsel, A., Vaidyanathan, K., et al.: A methodology for detection and estimation of software aging. In: Proceedings of Ninth International Symposium on Software Reliability Engineering (1998)Google Scholar
  20. 20.
    Jia, Y.F., et al.: On the relationship between software aging and related parameters. In: International Conference on Quality Software (2008)Google Scholar
  21. 21.
    Avritzer, A., Bondi, A., Grottke, M., et al.: Performance assurance via software rejuvenation: monitoring, statistics and algorithms. In: International Conference on Dependable Systems and Networks, DSN 2006 (2006)Google Scholar
  22. 22.
    Meng, H., Liu, J., Hei, X.: Modeling and optimizing periodically inspected software rejuvenation policy based on geometric sequences. Reliab. Eng. Syst. Saf. 133(133), 184–191 (2015)CrossRefGoogle Scholar
  23. 23.
    Grall, A., Dieulle, L., Berenguer, C., Roussignol, M.: Continuous time predictive maintenance scheduling for a deteriorating system. IEEE Trans. Reliab. 51(2), 150–155 (2001)zbMATHGoogle Scholar
  24. 24.
    Zhao, T.H., et al.: Application server rejuvenation policy research based on aging accumulative damage model. J. Syst. Simul. 18, 226–229 (2006)Google Scholar
  25. 25.
    Zhang, J.H., et al.: Approach of virtual machine failure recovery based on hidden Markov model. J. Softw. 25(11), 2702–2714 (2014)Google Scholar
  26. 26.
    Avritzer, A., Weyuker, E.J.: Monitoring smoothly degrading systems for increased dependability. Empirical Softw. Eng. 2(2), 59–77 (1997)CrossRefGoogle Scholar
  27. 27.
    Meyn, S., Tweedie, R.L.: Markov Chains and Stochastic Stability. Springer, Heidelberg (1993). p. xxviii+594CrossRefzbMATHGoogle Scholar
  28. 28.
    Noortwijk, J.M.V.: A survey of the application of gamma processes in maintenance. Reliab. Eng. Syst. Saf. 94(1), 2–21 (2009)CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.College of Electrical and Information EngineeringLanzhou University of TechnologyLanzhouPeople’s Republic of China
  2. 2.Division of Industrial and System EngineeringTaiyuan University of Science and TechnologyTaiyuanPeople’s Republic of China
  3. 3.School of Computer Science and Control EngineeringNorth University of ChinaTaiyuanPeople’s Republic of China

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