Abstract
Web Service play an important role in the Service-oriented Architecture (SOA), which is a new paradigm to implementing dynamic e-business solution, as Web services can be composed in an orchestrated manner by using Business Process Execution Language (BPEL). In this context, the performance of a Web service workflow is a very important factor for Business Process Re-engineering (BPR). A framework for the performance prediction and analysis of service-based applications from users’ perspectives was present in this paper. A historical time series for a specific performance is evaluated first in the framework. And then Particle Swarm Optimization based Back Propagation Neural Network (PSO-BPNN) model is constructed based on time series to predict the dynamic performance of workflow systems. When the predicted value is out of the preset range, we analyze the issues according to data of Quality of Service (QoS) which is detected at runtime, to find why cause service performance failure. Thus it suggests more suitable recovery strategies for service composition. To bring this approach to fruition we analyze a simple case study.
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References
Tan, W., Xu, Y., Xu, W., et al.: A methodology toward manufacturing grid-based virtual enterprise operation platform. Enterprise Information Systems 4(3), 283–309 (2010)
McGregor, C., Schiefer, J.: A Web-Service based framework for analyzing and measuring business performance. Information Systems and E-Business Management 2(1), 89–110 (2004)
Tan, W., Sun, Y., Li, L.X., Lu, G., Wang, T.: A trust service-oriented scheduling model for workflow applications in cloud computing. IEEE Systems Journal (2013). doi:10.1109/JSYST.2013.2260072
Gallotti, S., Ghezzi, C., Mirandola, R., Tamburrelli, G.: Quality prediction of service compositions through probabilistic model checking. In: Becker, S., Plasil, F., Reussner, R. (eds.) QoSA 2008. LNCS, vol. 5281, pp. 119–134. Springer, Heidelberg (2008)
Marzolla, M., Mirandola, R.: Performance prediction of Web service workflows. In: Overhage, S., Ren, X.-M., Reussner, R., Stafford, J.A. (eds.) QoSA 2007. LNCS, vol. 4880, pp. 127–144. Springer, Heidelberg (2008)
Zheng, Z., Lyu, M.R.: Personalized reliability prediction of Web services. ACM Transactions on Software Engineering and Methodology (TOSEM) 22(2), 12 (2013)
Silic, M., Delac, G., Srbljic, S.: Prediction of atomic web services reliability based on k-means clustering. In: Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering, pp. 70–80. ACM (2013)
Guo, F., Zhang, M.: Description and analyzing the reliability of web services composition based on petri nets. In: 2009 1st International Conference on Information Science and Engineering (ICISE), pp. 5329–5332. IEEE (2009)
Ahmed, W., Wu, Y.W.: Reliability prediction model for SOA using hidden markov model. In: 2013 8th ChinaGrid Annual Conference (ChinaGrid), pp. 40–45. IEEE (2013)
Vathsala, A.V., Mohanty, H.: Using HMM for predicting response time of web services. In: Proceedings of the CUBE International Information Technology Conference, pp. 520–525. ACM (2012)
Xie, C., Wang, X.: Reliability Prediction Model for Web Services Based on Control structure. Computer Science 38(B10), 92–95 (2011)
D’Ambrogio, A., Bocciarelli, P.: A model-driven approach to describe and predict the performance of composite services. In: Proceedings of the 6th International Workshop on Software and Performance, pp. 78–89. ACM (2007)
Zhang, M., Li, J., Xing, J., et al.: Quality of service prediction of multi-agent web service integration system based on grey meural network. Journal of Nanjing University (Natural Sciences) 2, 17 (2013)
Zadeh, M.H.: A self-healing architecture for web services based on failure prediction and a multi agent system. In: 2011 Fourth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT), pp. 48–52. IEEE (2011)
Hua, Z., Li, M., Zhao, J., et al.: Web Service QoS prediction method based on time series analysis. Journal of Frontiers of Computer Science and Technology 7(3), 218–226 (2013)
Hai, Y., Wang, Z., Liu, Z., et al.: Approach for web service QoS dynamic prediction. Journal of Nanjing University of Science and Technology 37(1), 52–59 (2013)
Nasridinov, A., Byun, J.Y., Park, Y.H.: A QoS-aware performance prediction for self-healing web service composition. In: 2012 Second International Conference on Cloud and Green Computing (CGC), pp. 799–803. IEEE (2012)
Leitner, P., Wetzstein, B., Rosenberg, F., Michlmayr, A., Dustdar, S., Leymann, F.: Runtime prediction of service level agreement violations for composite services. In: Dan, A., Gittler, F., Toumani, F. (eds.) ICSOC/ServiceWave 2009. LNCS, vol. 6275, pp. 176–186. Springer, Heidelberg (2010)
Maggi, F.M., Di Francescomarino, C., Dumas, M., et al.: Predictive Monitoring of Business Processes (2013). arXiv preprint arXiv:1312.4874
Zhu, Y., Wu, X., Zhang, P., et al.: Predicting failures in dynamic composite services with proactive monitoring technique. In: 2012 IEEE Eighth World Congress on Services (SERVICES), pp. 92–99. IEEE (2012)
Liu, B., Fan, Y.: Service-Oriented Workflow Performance Evaluation and Correlation Analysis for Key Performance Indicators. Computer Integrated Manufacturing System 14(1), 160–166 (2008)
Sun, Y., Tan, W., Li, L., et al.: SLA detective control model for workflow composition of cloud services. In: 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 165–171. IEEE (2013)
Tan, W.A., Shen, W., Zhao, J.: A methodology for dynamic enterprise process performance evaluation. Computers in Industry 58(5), 474–485 (2007)
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Tan, W., Li, L., Sun, Y. (2015). A Novel Performance Prediction Framework for Web Service Workflow Applications. In: Zu, Q., Hu, B., Gu, N., Seng, S. (eds) Human Centered Computing. HCC 2014. Lecture Notes in Computer Science(), vol 8944. Springer, Cham. https://doi.org/10.1007/978-3-319-15554-8_5
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DOI: https://doi.org/10.1007/978-3-319-15554-8_5
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