Abstract
The dynamic environment applications are the approaches for the construction of distributed business framework. The quality of service (QoS) banks of these systems of these systems depends on the Web services (WS) and Internet associations. Outlining productive and viable reliability prediction of WS have turned into an imperative issue. This paper focuses reliability of systems by using hidden Markov model (HMM) for the modeling of failure and prediction of Web service reliability. The forward--backward estimation-maximization is used to estimate the modeling parameters of HMM and by using Bayesian Information Criterion (BIC), model selection is done. The favorable circumstances and disadvantages of this approach concerning regular modeling are examined. Examination of these models is done on real Web service data. Regarding reliability prediction, the hidden Markov model performs better with respect to other regular models.
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References
Durand, J.-B., Gaudoin, O.: Software reliability modelling and prediction with hidden Markov chains. Stat. Model. 5(1), 75–93 (2005)
Chen, Y., Singpurwalla, N.D.: Unification of software reliability models by self-exciting point processes. Adv. Appl. Probab. 29, 337–352 (1997)
Cai, Z., et al.: Method on integrated reliability assessment of test data based on Duane model. In: 2014 International Conference on Reliability, Maintainability and Safety (ICRMS). IEEE (2014)
Ohishi, K., Okamura, H., Dohi, T.: Gompertz software reliability model: estimation algorithm and empirical validation. J. Syst. Softw. 82(3), 535–543 (2009)
Boland, P.J., Singh, H.: A birth-process approach to Moranda’s geometric software-reliability model. IEEE Trans. Reliab. 52(2), 168–174 (2003)
Gaudoin, O., Lavergne, C., Soler, J.-L.: A generalized geometric de-eutrophication software-reliability model. IEEE Trans. Reliab. 43(4), 536–541 (1994)
Honamore, S., Rath, S.K.: A web service reliability prediction using HMM and fuzzy logic models. Procedia Comput. Sci. 93, 886–892 (2016)
Khreich, W., et al.: A survey of techniques for incremental learning of HMM parameters. Inf. Sci. 197, 105–130 (2012)
Moon, T.K.: The expectation-maximization algorithm. IEEE Sig. Process. Mag. 13(6), 47–60 (1996)
Oudelha, M., Ainon, R.N.: HMM parameters estimation using hybrid Baum-Welch genetic algorithm. In: 2010 International Symposium on Information Technology, vol. 2. IEEE (2010)
Chang, I., Kim, S.W.: Modelling for identifying accident-prone spots: Bayesian approach with a Poisson mixture model. KSCE J. Civ. Eng. 16(3), 441–449 (2012)
Zheng, Z., Lyu, M.R.: Personalized reliability prediction of web services. ACM Trans. Softw. Eng. Methodol. (TOSEM) 22(2), 1–25 (2013)
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Allagi, S., Surasura, P. (2021). Predicting Reliability of Web Services Using Hidden Markov Model. In: Bhateja, V., Peng, SL., Satapathy, S.C., Zhang, YD. (eds) Evolution in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_16
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DOI: https://doi.org/10.1007/978-981-15-5788-0_16
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