Skip to main content
SpringerLink
Log in

Automated Prediction of the QoS of Service Orchestrations: PASO at Work

  • Conference paper
  • First Online:
Advances in Service-Oriented and Cloud Computing (ESOCC 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 567))

Included in the following conference series:

Abstract

Predicting the QoS of a service orchestration is not easy because of the a priori undetermined behaviour of invoked services, and because of the non-determinism (alternatives, unbounded iterations, fault handling) and complex structure (dependencies, correlations) of the workflow defining a service orchestration. In this paper we illustrate the practical usefulness of a probabilistic analyser of service orchestrations (PASO) by showing how it can be fruitfully exploited to predict the QoS of service orchestrations.

Work partly supported by the EU-FP7-ICT-610531 SeaClouds project.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Notes

  1. 1.

    The source code of PASO is available at https://github.com/upi-bpel/paso.

  2. 2.

    The interest reader can refer to [11] for a thorough description of the analysis implemented by PASO.

  3. 3.

    PASO is able to analyse a subset of WS-BPEL structural (sequence, flow, if, while, scope, and faultHandlers) and basic (invoke and assign) activities. Other basic activities (like receive or reply) are considered by PASO successfully executable with zero cost.

  4. 4.

    These probabilities may be deduced from Service Level Agreements (SLAs), or statistically inferred from data such as logs or performance counters if available.

  5. 5.

    We performed one million iterations of PASO for each group of questions.

References

  1. Papazoglou, M.: Web Services: Principles and Technology, 2nd edn. Pearson Education, Toronto (2012)

    Google Scholar 

  2. Weske, M.: Business Process Management: Concepts, Languages, Architectures. Springer, New York (2007)

    Google Scholar 

  3. Kim, E., et al.: Web Services Quality Factors. Candidate OASIS Standard Version 1.0. http://docs.oasis-open.org/wsqm/WS-Quality-Factors/v1.0/cos01/WS-Quality-Factors-v1.0-cos01.html

  4. Kritikos, K., Pernici, B., Plebani, P., Cappiello, C., Comuzzi, M., Benrernou, S., Brandic, I., Kertész, A., Parkin, M., Carro, M.: A survey on service quality description. ACM Comput. Surv. (CSUR) 46(1), 1–64 (2013)

    Article  Google Scholar 

  5. Filieri, A., Maggio, M., Angelopoulos, K., D’Ippolito, N., Gerostathopoulos, I., Hempel, A., Hoffmann, H., Jamshidi, P., Kalyvianaki, E., Klein, C., et al.: Software engineering meets control theory. In: Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (2015)

    Google Scholar 

  6. Ravishankar, V., Baskaran, R.: A compendium on service oriented architecture and service level agreements. Int. J. Comput. Appl. 40(1), 13–17 (2013)

    Google Scholar 

  7. Mukherjee, D., Jalote, P., Gowri Nanda, M.: Determining QoS of WS-BPEL compositions. In: Bouguettaya, A., Krueger, I., Margaria, T. (eds.) ICSOC 2008. LNCS, vol. 5364, pp. 378–393. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  8. Jordan, D., Evdemon, J., Alves, A., Arkin, A., Askary, S., Barreto, C., Bloch, B., Curbera, F., Ford, M., Goland, Y., et al.: Web services business process execution language version 2.0. OASIS standard 11, April 2007

    Google Scholar 

  9. Mukherjee, D.: QOS IN WS-BPEL PROCESSES. Master’s thesis, Indian Institute of Technology, Delhi, May 2008

    Google Scholar 

  10. Bartoloni, L., Brogi, A., Ibrahim, A.: Probabilistic prediction of the QoS of service orchestrations: a truly compositional approach. In: Franch, X., Ghose, A.K., Lewis, G.A., Bhiri, S. (eds.) ICSOC 2014. LNCS, vol. 8831, pp. 378–385. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  11. Bartoloni, L., Brogi, A., Ibrahim, A.: Predicting the QoS of service orchestrations. Technical report, Dipartimento di Informatica, University of Pisa, Italy, March 2015. http://eprints.adm.unipi.it/2329/1/Unipi_TR.pdf (Submitted for publication)

  12. Bouillard, A., Rosario, S., Benveniste, A., Haar, S.: Monotonicity in service orchestrations. In: Franceschinis, G., Wolf, K. (eds.) PETRI NETS 2009. LNCS, vol. 5606, pp. 263–282. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  13. Leitner, P., Ferner, J., Hummer, W., Dustdar, S.: Data-driven and automated prediction of service level agreement violations in service compositions. Distrib. Parallel Databases 31(3), 447–470 (2013)

    Article  Google Scholar 

  14. Becker, S., Koziolek, H., Reussner, R.: Model-based performance prediction with the palladio component model. In: Proceedings of the 6th International Workshop on Software and Performance, pp. 54–65. ACM (2007)

    Google Scholar 

  15. van Hoorn, A., Rohr, M., Hasselbring, W., Waller, J., Ehlers, J., Frey, S., Kieselhorst, D.: Continuous monitoring of software services: design and application of the kieker framework. Research report, Kiel University, November 2009

    Google Scholar 

  16. Kounev, S., Brosig, F., Huber, N.: The Descartes Modeling Language. Technical report, Department of Computer Science, University of Wuerzburg, October 2014

    Google Scholar 

  17. Cardoso, A.J.S.: Quality of service and semantic composition of workflows. Ph.D. thesis, University of Georgia (2002)

    Google Scholar 

  18. Zheng, H., Zhao, W., Yang, J., Bouguettaya, A.: QoS analysis for web service compositions with complex structures. IEEE Trans. Serv. Comput. 6(3), 373–386 (2013)

    Article  Google Scholar 

  19. Ivanović, D., Carro, M., Kaowichakorn, P.: Towards QoS prediction based on composition structure analysis and probabilistic models. In: Franch, X., Ghose, A.K., Lewis, G.A., Bhiri, S. (eds.) ICSOC 2014. LNCS, vol. 8831, pp. 394–402. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  20. Bhat, S., Borgström, J., Gordon, A.D., Russo, C.: Deriving probability density functions from probabilistic functional programs. In: Piterman, N., Smolka, S.A. (eds.) TACAS 2013 (ETAPS 2013). LNCS, vol. 7795, pp. 508–522. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  21. Stuhlmüller, A., Goodman, N.D.: A dynamic programming algorithm for inference in recursive probabilistic programs. In: Second Statistical Relational AI workshop at UAI 2012 (StaRAI-12) (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Pisa, Pisa, Italy

    Leonardo Bartoloni, Antonio Brogi & Ahmad Ibrahim

Authors
  1. Leonardo Bartoloni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Antonio Brogi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmad Ibrahim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmad Ibrahim .

Editor information

Editors and Affiliations

  1. DICIEAMA, University of Messina, Messina, Italy

    Antonio Celesti

  2. Software Evolution and Architecture Lab, University of Zürich Software Evolution and Architecture Lab, Zürich, Switzerland

    Philipp Leitner

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Bartoloni, L., Brogi, A., Ibrahim, A. (2016). Automated Prediction of the QoS of Service Orchestrations: PASO at Work. In: Celesti, A., Leitner, P. (eds) Advances in Service-Oriented and Cloud Computing. ESOCC 2015. Communications in Computer and Information Science, vol 567. Springer, Cham. https://doi.org/10.1007/978-3-319-33313-7_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-33313-7_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33312-0

  • Online ISBN: 978-3-319-33313-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation