Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Data-driven and automated prediction of service level agreement violations in service compositions

Abstract

Service Level Agreements (SLAs), i.e., contractually binding agreements between service providers and clients, are gaining momentum as the main discriminating factor between service implementations. For providers, SLA compliance is of utmost importance, as violations typically lead to penalty payments or reduced customer satisfaction. In this paper, we discuss approaches to predict violations a priori. This allows operators to take timely remedial actions, and prevent SLA violations before they have occurred. We discuss data-driven, statistical approaches for both, instance-level prediction (SLA compliance prediction for an ongoing business process instance) and forecasting (compliance prediction for future instances). We present an integrated framework, and numerically evaluate our approach based on a case study from the manufacturing domain.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Listing 1
Fig. 10
Fig. 11

Notes

  1. 1.

    http://www.microsoft.com/download/en/details.aspx?id=31.

  2. 2.

    http://msdn.microsoft.com/en-us/netframework/aa663328.

  3. 3.

    http://esper.codehaus.org/about/nesper/nesper.html.

  4. 4.

    http://esper.codehaus.org/.

  5. 5.

    http://www.mysql.com/products/community/.

  6. 6.

    http://www.csscript.net/.

  7. 7.

    http://msdn.microsoft.com/en-us/library/ms735967(VS.90).aspx.

References

  1. 1.

    Amin, A., Colman, A., Grunske, L.: An approach to forecasting QoS attributes of web services based on ARIMA and GARCH models. In: Proceedings of the 2012 IEEE International Conference on Web Services, pp. 74–81. IEEE Computer Society, Washington, DC (2012). doi:10.1109/ICWS.2012.37

  2. 2.

    Andrieux, A., Czajkowski, K., Dan, A., Keahey, K., Ludwig, H., Nakata, T., Pruyne, J., Rofrano, J., Tuecke, S., Xu, M.: Web Services Agreement Specification (WS-Agreement). Tech. rep., Open Grid Forum (OGF) (2006). http://www.gridforum.org/documents/GFD.107.pdf, Last Visited: 2011-07-19

  3. 3.

    Balaguer, E., Palomares, A., Soria, E., Martín-Guerrero, J.D.: Predicting service request in support centers based on nonlinear dynamics, ARMA modeling and neural networks. Expert Syst. Appl. 34(1), 665–672 (2008). doi:10.1016/j.eswa.2006.10.003

  4. 4.

    Bodenstaff, L., Wombacher, A., Reichert, M., Jaeger, M.: Monitoring dependencies for SLAs: the MoDe4SLA approach. In: Proceedings of the 2008 IEEE International Conference on Services Computing (SCC’08), pp. 21–29. IEEE Computer Society, Washington, DC (2008). http://portal.acm.org/citation.cfm?id=1447562.1447847. doi:10.1109/SCC.2008.120

  5. 5.

    Bodenstaff, L., Wombacher, A., Reichert, M., Jaeger, M.C.: Analyzing impact factors on composite services. In: Proceedings of the 2009 IEEE International Conference on Services Computing (SCC’09), pp. 218–226. IEEE Computer Society, Los Alamitos (2009)

  6. 6.

    Box, G.E.P., Jenkins, G.M.: Time Series Analysis—Forecasting and Control. Holden-Day (1976)

  7. 7.

    Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging it platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25(6), 599–616 (2009). doi:10.1016/j.future.2008.12.001

  8. 8.

    Canfora, G., Di Penta, M., Esposito, R., Villani, M.L.: QoS-aware replanning of composite web services. In: Proceedings of the IEEE International Conference on Web Services (ICWS’05), pp. 121–129. IEEE Computer Society, Washington, DC (2005). doi:10.1109/ICWS.2005.96

  9. 9.

    Castellanos, M., Casati, F., Dayal, U., Shan, M.C.: Intelligent management of SLAs for composite web services. In: Databases in Networked Information Systems (2003)

  10. 10.

    Dan, A., Davis, D., Kearney, R., Keller, A., King, R.P., Kuebler, D., Ludwig, H., Polan, M., Spreitzer, M., Youssef, A.: Web services on demand: WSLA-driven automated management. IBM Systems Journal 43, 136–158 (2004). doi:10.1147/sj.431.0136

  11. 11.

    Dongen, B.F., Crooy, R.A., Aalst, W.M.: Cycle time prediction: when will this case finally be finished? In: Proceedings of the 2008 OTM Confederated International Conferences, pp. 319–336. Springer, Berlin (2008)

  12. 12.

    Dustdar, S., Schreiner, W.: A survey on web services composition. International Journal of Web and Grid Services 1(1), 1–30 (2005)

  13. 13.

    Emeakaroha, V.C., Brandic, I., Maurer, M., Dustdar, S.: Low level metrics to high level slas - lom2his framework: bridging the gap between monitored metrics and sla parameters in cloud environments. In: Proc. Int High Performance Computing and Simulation (HPCS) Conf, pp. 48–54 (2010). doi:10.1109/HPCS.2010.5547150

  14. 14.

    Emeakaroha, V.C., Netto, M.A.S., Calheiros, R.N., Brandic, I., Buyya, R., De Rose, C.A.F.: Towards autonomic detection of SLA violations in cloud infrastructures. Future Gener. Comput. Syst. (2011). doi:10.1016/j.future.2011.08.018

  15. 15.

    Ferner, J.: Using Time Series Analysis for Predicting Service Level Agreement Violations in Service Compositions. Master’s thesis, Vienna University of Technology (2012)

  16. 16.

    Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29, 131–163 (1997). http://portal.acm.org/citation.cfm?id=274158.274161. doi:10.1023/A:1007465528199

  17. 17.

    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explorations 11(1), 10–18 (2009). http://portal.acm.org/citation.cfm?id=1656274.1656278. doi:10.1145/1656274.1656278

  18. 18.

    Hielscher, J., Kazhamiakin, R., Metzger, A., Pistore, M.: A framework for proactive self-adaptation of service-based applications based on online testing. In: Proceedings of the 1st European Conference on Towards a Service-Based Internet (ServiceWave’08), pp. 122–133. Springer, Berlin (2008)

  19. 19.

    Hummer, W., Raz, O., Shehory, O., Leitner, P., Dustdar, S.: Testing of Data-Centric and Event-Based Dynamic Service Compositions. Softw. Test. Verif. Reliab. (2013, to appear)

  20. 20.

    Inzinger, C., Hummer, W., Satzger, B., Leitner, P., Dustdar, S.: Identifying incompatible service implementations using pooled decision trees. In: 28th ACM Symposium on Applied Computing (SAC’13), DADS Track (2013)

  21. 21.

    Ivanovic, D., Carro, M., Hermenegildo, M.: An initial proposal for data-aware resource analysis of orchestrations with applications to predictive monitoring. In: Proceedings of the 2009 International Conference on Service-Oriented Computing (ICSOC’09), pp. 414–424. Springer, Berlin (2009). http://portal.acm.org/citation.cfm?id=1926618.1926662

  22. 22.

    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan-Kaufmann, San Mateo (1993)

  23. 23.

    Jain, A.K., Mao, J., Mohiuddin, K.M.: Artificial neural networks: a tutorial. IEEE Computer 29, 31–44 (1996). doi:10.1109/2.485891

  24. 24.

    Juszczyk, L., Dustdar, S.: Script-based generation of dynamic testbeds for SOA. In: Proceedings of the 2010 IEEE International Conference on Web Services (ICWS’10), pp. 195–202. IEEE Computer Society, Washington, DC (2010). doi:10.1109/ICWS.2010.75

  25. 25.

    Keller, A., Ludwig, H.: The WSLA framework: specifying and monitoring service level agreements for web services. Journal on Network and Systems Management 11, 57–81 (2003). http://portal.acm.org/citation.cfm?id=635430.635442. doi:10.1023/A:1022445108617

  26. 26.

    Leitner, P., Hummer, W., Dustdar, S.: Cost-based optimization of service compositions. IEEE Trans. Serv. Comput. 99 (2011). http://doi.ieeecomputersociety.org/10.1109/TSC.2011.53

  27. 27.

    Leitner, P., Michlmayr, A., Rosenberg, F., Dustdar, S.: Monitoring, prediction and prevention of SLA violations in composite services. In: Proceedings of the IEEE International Conference on Web Services (ICWS’10), pp. 369–376. IEEE Computer Society, Los Alamitos (2010)

  28. 28.

    Leitner, P., Wetzstein, B., Rosenberg, F., Michlmayr, A., Dustdar, S., Leymann, F.: Runtime prediction of service level agreement violations for composite services. In: Proceedings of the 3rd Workshop on Non-Functional Properties and SLA Management in Service-Oriented Computing (NFPSLAM-SOC’09), pp. 176–186. Springer, Berlin (2009). http://portal.acm.org/citation.cfm?id=1926618.1926639

  29. 29.

    Liu, Y., Gorton, I., Zhu, L.: Performance prediction of service-oriented applications based on an enterprise service bus. In: Proceedings of the 31st Annual International Computer Software and Applications Conference, COMPSAC’07, vol. 01, pp. 327–334. IEEE Computer Society, Washington, DC (2007). doi:10.1109/COMPSAC.2007.166

  30. 30.

    Luckham, D.: The Power of Events: an Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison-Wesley, Reading (2002)

  31. 31.

    Menascé, D.A.: QoS issues in web services. IEEE Internet Computing 6(6), 72–75 (2002). doi:10.1109/MIC.2002.1067740

  32. 32.

    Metzger, A., Sammodi, O., Pohl, K., Rzepka, M.: Towards pro-active adaptation with confidence: augmenting service monitoring with online testing. In: Proceedings of the 2010 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems (SEAMS’10), pp. 20–28. ACM, New York (2010). doi:10.1145/1808984.1808987

  33. 33.

    Michlmayr, A., Rosenberg, F., Leitner, P., Dustdar, S.: Advanced event processing and notifications in service runtime environments. In: Proceedings of the 2nd International Conference on Distributed Event-Based Systems (DEBS’08), pp. 115–125. ACM, New York (2008). doi:10.1145/1385989.1386004

  34. 34.

    Michlmayr, A., Rosenberg, F., Leitner, P., Dustdar, S.: Comprehensive QoS monitoring of web services and event-based SLA violation detection. In: Proceedings of the 4th International Workshop on Middleware for Service Oriented Computing (MWSOC’09), pp. 1–6. ACM, New York (2009)

  35. 35.

    Michlmayr, A., Rosenberg, F., Leitner, P., Dustdar, S.: End-to-end support for QoS-aware service selection, binding, and mediation in VRESCo. IEEE Transactions on Services Computing 3, 193–205 (2010)

  36. 36.

    Oberortner, E., Zdun, U., Dustdar, S.: Patterns for measuring performance-related QoS properties in distributed systems. In: Proceedings of the 17th Conference on Pattern Languages of Programs (PLoP) (2010)

  37. 37.

    Papazoglou, M.P., Traverso, P., Dustdar, S., Leymann, F.: Service-oriented computing: state of the art and research challenges. IEEE Computer 40(11), 38–45 (2007)

  38. 38.

    Pruscha, H., G”ottlein, A.: Forecasting of categorical time series using a regression model. Economic Quality Control 18(2), 223–240 (2003). http://www.heldermann-verlag.de/eqc/eqc18/eqc18014.pdf

  39. 39.

    Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)

  40. 40.

    Quinlan, J.R.: Improved use of continuous attributes in C4.5. Journal of Artificial Intelligence Research 4, 77–90 (1996)

  41. 41.

    R Development Core Team: R: a Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2008). http://www.R-project.org. ISBN 3-900051-07-0

  42. 42.

    Richardson, L., Ruby, S.: RESTful Web Services. O’Reilly (2007)

  43. 43.

    Rijsbergen, C.J.V.: In: Information Retrieval. Butterworths, Stoneham (1979)

  44. 44.

    Sahai, A., Machiraju, V., Sayal, M., Moorsel, A.P.A.V., Casati, F.: Automated SLA monitoring for web services. In: Proceedings of the 13th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management (DSOM) (2002)

  45. 45.

    Shumway, R.H., Stoffer, D.S.: Time Series Analysis and Its Applications. Springer, Berlin (2010)

  46. 46.

    Skene, J., Lamanna, D.D., Emmerich, W.: Precise service level agreements. In: Proceedings of the 26th International Conference on Software Engineering (ICSE’04), pp. 179–188. IEEE Computer Society, Washington, DC (2004). http://portal.acm.org/citation.cfm?id=998675.999422

  47. 47.

    Tosic, V., Ma, W., Pagurek, B., Esfandiari, B.: Web service offerings infrastructure (WSOI)—a management infrastructure for XML web services. In: Proceedings of the IEEE/IFIP Network Operations and Management Symposium (NOMS’04), pp. 817–830 (2004)

  48. 48.

    Tosic, V., Pagurek, B., Patel, K., Esfandiari, B., Ma, W.: Management applications of the web service offerings language (WSOL). Information Systems 30(7), 564–586 (2005). doi:10.1016/j.is.2004.11.005

  49. 49.

    Van Der Aalst, W.M.P., Hofstede, A.H.M.T., Weske, M.: Business process management: a survey. In: Proceedings of the 2003 International Conference on Business Process Management, BPM’03, pp. 1–12. Springer, Berlin (2003). http://dl.acm.org/citation.cfm?id=1761141.1761143

  50. 50.

    Wetzstein, B., Leitner, P., Rosenberg, F., Brandic, I., Dustdar, S., Leymann, F.: Monitoring and analyzing influential factors of business process performance. In: Proceedings of the 13th IEEE International Conference on Enterprise Distributed Object Computing (EDOC’09), pp. 118–127. IEEE Press, Piscataway (2009). http://portal.acm.org/citation.cfm?id=1719357.1719370

  51. 51.

    Wetzstein, B., Leitner, P., Rosenberg, F., Dustdar, S., Leymann, F.: Identifying influential factors of business process performance using dependency analysis. Enterprise Information Systems 4(3), 1–8 (2010)

  52. 52.

    Wetzstein, B., Strauch, S., Leymann, F.: Measuring performance metrics of WS-BPEL service compositions. In: Proceedings of the Fifth International Conference on Networking and Services (ICNS’09). IEEE Computer Society, Los Alamitos (2009)

  53. 53.

    Zeng, L., Lei, H., Chang, H.: Monitoring the QoS for web services. In: Proceedings of the 5th International Conference on Service-Oriented Computing (ICSOC’07), pp. 132–144. Springer, Berlin (2007)

  54. 54.

    Zeng, L., Lingenfelder, C., Lei, H., Chang, H.: Event-driven quality of service prediction. In: Proceedings of the 6th International Conference on Service-Oriented Computing (ICSOC’08), pp. 147–161. Springer, Berlin (2008)

Download references

Acknowledgements

The research leading to these results has received funding from the European Community’s Seventh Framework Program [FP7/2007-2013] under grant agreement 257483 (Indenica), as well as from the Austrian Science Fund (FWF) under project references P23313-N23 (Audit 4 SOAs).

Author information

Correspondence to Philipp Leitner.

Additional information

Communicated by Amit Sheth.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Leitner, P., Ferner, J., Hummer, W. et al. Data-driven and automated prediction of service level agreement violations in service compositions. Distrib Parallel Databases 31, 447–470 (2013). https://doi.org/10.1007/s10619-013-7125-7

Download citation

Keywords

  • Service composition
  • Service level agreements
  • Quality prediction