Open Access
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
Article Number 03045
Number of page(s) 5
Section Algorithm Study and Mathematical Application
Published online 19 November 2018
  1. H. Wang, L. Wang, Q. Yu, Z. Zheng, A. Bouguettaya, M.R. Lyu, Online reliability prediction via motifs-based dynamic Bayesian networks for service-oriented systems, IEEE Trans Softw Eng, 43, 556-579 (2017) [CrossRef] [Google Scholar]
  2. M. Grottke, K.S. Trivedi, Fighting bugs: Remove, retry, replicate, and rejuvenate, Computer, 40, 107–109 (2007) [Google Scholar]
  3. V.P. Koutras, A.N. Platis, VoIP availability and service reliability through software rejuvenation policies, Proc. of 2nd Int Conf Dependability of Computer Systems, 262–269 (2007) [Google Scholar]
  4. J. Alonso, R. Matias, E. Vicente, A. Maria, K.S. Trivedi, A comparative experimental study of software rejuvenation overhead, Perform Eval, 70, 231–250 (2013) [Google Scholar]
  5. J. Zheng, H. Okamura, L. Li, T. Dohi, A comprehensive evaluation of software rejuvenation policies for transaction systems with Markovian arrivals, IEEE Trans Rel, 66, 1157–1177 (2017) [CrossRef] [Google Scholar]
  6. H. Okamura, T. Dohi, Dynamic software rejuvenation policies in a transaction-based system under Markovian arrival processes, Perform Eval, 70, 197–211 (2013) [Google Scholar]
  7. H. Okamura, J. Zheng, T. Dohi, A statistical framework on software aging modelling with continuous-time hidden Markov model, Proc. of 36th Int Symp Rel Dist Systems, 114–123 (2017) [Google Scholar]
  8. S. Garg, S. Pfening, A. Puliafito, M. Telek, K.S. Trivedi, Analysis of preventive maintenance in transactions based software systems, IEEE Trans Computers, 47, 96–107 (1998) [CrossRef] [Google Scholar]
  9. A. Bobbio, M. Sereno, C. Anglano, Fine grained software degradation models for optimal rejuvenation policies, Perform Eval, 46, 45–62 (2001) [Google Scholar]
  10. H. Okamura, H. Fujio, T. Dohi, Fine-grained shock models to rejuvenate software systems, IEICE Trans Info Syst, E86-D, 2165–2171 (2003) [Google Scholar]
  11. H. Okamura, S. Miyahara, T. Dohi, S. Osaki, Performance evaluation of workload-based software rejuvenation scheme, IEICE Trans Info Syst, E84-D, 1368–1375 (2001) [Google Scholar]
  12. W. Xie, Y. Hong, K.S. Trivedi, Analysis of a two-level software rejuvenation policy, Rel Eng Syst Saf, 87, 13–22 (2005) [CrossRef] [Google Scholar]
  13. G. Ning, J. Zhao, Y. Lou, J. Alonso, R. Matias, K.S. Trivedi, B.B. Yin, K.Y. Cai, Optimization of two-granularity software rejuvenation policy based on the Markov regenerative process, IEEE Trans Rel, 65, 1630–1646 (2016) [CrossRef] [Google Scholar]
  14. T. Dohi, J. Zheng, H. Okamura, K.S. Trivedi, Optimal periodic software rejuvenation policies based on interval reliability criteria, Reliab Eng Syst Saf (2018, in publication process) [Google Scholar]
  15. H. Suzuki, T. Dohi, K. Goseva-Popstojanova, K.S. Trivedi, Analysis of multistep failure models with periodic software rejuvenation, Advances in Stochastic Modelling, 85–108 (Notable Publications Inc., 2002) [Google Scholar]

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