Dynamically Selecting Composition Algorithms for Economical Composition as a Service

  • Immanuel Trummer
  • Boi Faltings
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7084)


Various algorithms have been proposed for the problem of quality-driven service composition. They differ by the quality of the resulting executable processes and by their processing costs. In this paper, we study the problem of service composition from an economical point of view and adopt the perspective of a Composition as a Service provider. Our goal is to minimize composition costs while delivering executable workflows of a specified average quality. We propose to dynamically select different composition algorithms for different workflow templates based upon template structure and workflow priority. For evaluating our selection algorithm, we consider two classic approaches to quality-driven composition, genetic algorithms and integer linear programming with different parameter settings. An extensive experimental evaluation shows significant gains in efficiency when dynamically selecting between different composition algorithms instead of using only one algorithm.


Quality-Driven Service Composition Composition as a Service Dynamic Algorithm Selection 


  1. 1.
    Amazon elastic compute cloud,
  2. 2.
  3. 3.
    Ardagna, D., Pernici, B.: Adaptive service composition in flexible processes. IEEE Transactions on Software Engineering, 369–384 (2007)Google Scholar
  4. 4.
    Blake, M., Tan, W., Rosenberg, F.: Composition as a service (web-scale workflow). Internet Computing 14(1), 78–82 (2010)CrossRefGoogle Scholar
  5. 5.
    Canfora, G., Di Penta, M., Esposito, R., Villani, M.: An approach for QoS-aware service composition based on genetic algorithms. In: Conf. on Genetic and Evolutionary Computation, pp. 1069–1075. ACM (2005)Google Scholar
  6. 6.
    Comes, D., Baraki, H., Reichle, R., Zapf, M., Geihs, K.: Heuristic Approaches for QoS-Based Service Selection. In: Maglio, P.P., Weske, M., Yang, J., Fantinato, M. (eds.) ICSOC 2010. LNCS, vol. 6470, pp. 441–455. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Dumas, M., García-Bañuelos, L., Polyvyanyy, A., Yang, Y., Zhang, L.: Aggregate Quality of Service Computation for Composite Services. In: Maglio, P.P., Weske, M., Yang, J., Fantinato, M. (eds.) ICSOC 2010. LNCS, vol. 6470, pp. 213–227. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Koch, R.: Das 80-20-Prinzip. Campus-Verl. (1998)Google Scholar
  9. 9.
    Lawler, E.: Fast approximation algorithms for knapsack problems. In: 18th Annual Symposium on Foundations of Computer Science, 1977, pp. 206–213. IEEE (1977)Google Scholar
  10. 10.
    Rosenberg, F., Leitner, P., Michlmayr, A., Celikovic, P., Dustdar, S.: Towards composition as a service-a quality of service driven approach. In: Int. Conf. on Data Engineering, pp. 1733–1740. IEEE (2009)Google Scholar
  11. 11.
    Trummer, I., Faltings, B.: Optimizing the Tradeoff between Discovery, Composition, and Execution Cost in Service Composition. In: Int. Conf. on Web Services (2011)Google Scholar
  12. 12.
    Zeng, L., Benatallah, B., Ngu, A., Dumas, M., Kalagnanam, J., Chang, H.: QoS-aware middleware for web services composition. IEEE Transactions on Software Engineering 30(5), 311–327 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Immanuel Trummer
    • 1
  • Boi Faltings
    • 1
  1. 1.Artificial Intelligence LaboratoryEcole Polytechnique Fédérale de LausanneSwitzerland

Personalised recommendations