Parallel Adaptation of Multiple Service Composition Instances

  • Rafael Roque AschoffEmail author
  • Andrea Zisman
  • Pedro Alexandre


Existing approaches for adaptation of service compositions do not consider the fact that common services can be used in different compositions, and, therefore, a problem that may be identified in one composition could be used to predict unwanted situations in other compositions. In this paper, we propose a parallel and proactive adaptation framework that supports proactive adaptation in multiple service composition instances at the same time. In the framework, events observed for one particular service composition instance are shared between all composition instances executed in parallel in order to better predict problems and rectify them in all necessary instances, when possible. The parallel characteristic of the framework also supports balancing the load among candidate service operations, and, therefore, it considers the maximum expected service operation throughput between the compositions. A prototype tool has been implemented to illustrate and evaluate the framework in different scenarios.


  1. 1.
    Ardagna, D., Comuzzi, M., Mussi, E., Pernici, B., Plebani, P.: PAWS: A framework for executing adaptive web-service processes. IEEE Softw. 24(6), 39–46 (2007)CrossRefGoogle Scholar
  2. 2.
    Aschoff, R., Zisman, A.: QoS-driven proactive adaptation of service composition. In: ICSOC’11, pp. 421–435 (2011)CrossRefGoogle Scholar
  3. 3.
    Aschoff, R., Zisman, A.: Proactive adaptation of service composition. In: SEAMS’12, pp. 1–10 (2012)Google Scholar
  4. 4.
    Baresi, L., Di Nitto, E., Ghezzi, C., Guinea, S.: A framework for the deployment of adaptable web service compositions. SOCA 1(1), 75–91 (2007)CrossRefGoogle Scholar
  5. 5.
    Dai, Y., Yang, L., Zhang, B.: QoS-driven self-healing web service composition based on performance prediction. J. Comput. Sci. Technol. 24(2), 250–261 (2009)CrossRefGoogle Scholar
  6. 6.
    Di Nitto, E., Ghezzi, C., Metzger, A., Papazoglou, M., Pohl, K.: A journey to highly dynamic, self-adaptive service-based applications. ASE 15(3), 313–341 (2008)Google Scholar
  7. 7.
    Dustdar, S., Papazoglou, M.P.: Services and service composition – an introduction (services und service komposition – eine einführung). Inf. Technol. 50(2), 86–92 (2009)Google Scholar
  8. 8.
    Guinea, S., Kecskemeti, G., Marconi, A., Wetzstein, B.: Multi-layered monitoring and adaptation. In: ICSOC’11 (2011). CrossRefGoogle Scholar
  9. 9.
    Kazhamiakin, R., Wetzstein, B., Karastoyanova, D., Pistore, M., Leymann, F.: Adaptation of service-based applications based on process quality factor analysis. In: LNCS’09 (2009)Google Scholar
  10. 10.
    Leitner, P., Michlmayr, A., Rosenberg, F., Dustdar, S.: Monitoring, prediction and prevention of SLA violations in composite services. In: ICWS’10 (2010)Google Scholar
  11. 11.
    Metzger, A., Sammodi, O., Pohl, K., Rzepka, M.: Towards pro-active adaptation with confidence: augmenting service monitoring with online testing. In: SEAMS’10 (2010).
  12. 12.
    Moser, O., Rosenberg, F., Dustdar, S.: Non-intrusive monitoring and service adaptation for WS-BPEL. In: WWW’08 (2008).
  13. 13.
    Natrella, M.: e-Handbook of Statistical Methods. Nist/Sematech (2010).
  14. 14.
    Papazoglou, M.P., Traverso, P., Dustdar, S., Leymann, F.: Service-oriented computing: a research roadmap. Int. J. Coop. Inf. Syst. 17(2), 223–255 (2008)CrossRefGoogle Scholar
  15. 15.
    Pernici, B.: Self-healing systems and web services: the WS-DIAMOND approach. In: LNBIP’09 (2009)CrossRefGoogle Scholar
  16. 16.
    Pistore, M., Marconi, A., Bertoli, P., Traverso, P.: Automated composition of web services by planning at the knowledge level. In: IJCAI’05 (2005)Google Scholar
  17. 17.
    Popescu, R., Staikopoulos, A., Liu, P., Brogi, A., Clarke, S.: Taxonomy-driven adaptation of multi-layer applications using templates. In: SASO’10 (2010).
  18. 18.
    Saboohi, H., Amini, A., Herawan, T., Kareem, S.: Failure recovery of composite semantic services using expiration times. In: Herawan, T., Deris, M.M., Abawajy, J. (eds.) Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013), Lecture Notes in Electrical Engineering, vol. 285, pp. 683–690. Springer, Singapore (2014). Google Scholar
  19. 19.
    Tosi, D., Denaro, G., Pezze, M.: Towards autonomic service-oriented applications. Int. J. Autom. Comput. 1, 58–80 (2009). CrossRefGoogle Scholar
  20. 20.
    Web Services Business Process Execution Language (WS-BPEL) Version 2.0.: Organization for the Advancement of Structured Information Standards (OASIS) (2007).
  21. 21.
    Zengin, A., Kazhamiakin, R., Pistore, M.: Clam: cross-layer management of adaptation decisions for service-based applications. In: ICWS’11 (2011).
  22. 22.
    Zisman, A., Spanoudakis, G., Dooley, J., Siveroni, I.: Proactive and reactive runtime service discovery: A framework and its evaluation. IEEE Trans. Softw. Eng. 39(7), 954–974 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Rafael Roque Aschoff
    • 1
    Email author
  • Andrea Zisman
    • 2
  • Pedro Alexandre
    • 3
  1. 1.Federal Institute of Pernambuco - IFPEPernambucoBrazil
  2. 2.The Open UniversityMilton KeynesUK
  3. 3.University of Sao PauloSão PauloBrazil

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