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Optimizing Decisions in Web Services Orchestrations

  • Ajay Kattepur
  • Albert Benveniste
  • Claude Jard
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7084)

Abstract

Web services orchestrations conventionally employ exhaustive comparison of runtime quality of service (QoS) metrics for decision making. The ability to incorporate more complex mathematical packages are needed, especially in case of workflows for resource allocation and queuing systems. By modeling such optimization routines as service calls within orchestration specifications, techniques such as linear programming can be conveniently invoked by non-specialist workflow designers. Leveraging on previously developed QoS theory, we propose the use of a high-level flexible query procedure for embedding optimizations in languages such as Orc. The Optima site provides an extension to the sorting and pruning operations currently employed in Orc. Further, the lack of an objective technique for consolidating QoS metrics is a problem in identifying suitable cost functions. We employ the analytical hierarchy process (AHP) to generate a total ordering of QoS metrics across various domains. With constructs for ensuring consistency over subjective judgements, the AHP provides a suitable technique for producing objective cost functions. Using the Dell Supply Chain example, we demonstrate the feasibility of decision making through optimization routines, specially when the control flow is QoS dependent.

Keywords

Web Services QoS Optimization Orc AHP 

References

  1. 1.
    Hwang, S.Y., Wang, H., Tang, J., Srivastava, J.: A probabilistic approach to modeling and estimating the QoS of web-services-based workflows. Elsevier Information Sciences 177, 5484–5503 (2007)CrossRefzbMATHGoogle Scholar
  2. 2.
    Rosario, S., Benveniste, A., Haar, S., Jard, C.: Probabilistic QoS and Soft Contracts for Transaction-Based Web Services Orchestrations. IEEE Trans. on Services Computing 1(4), 187–200 (2008)CrossRefGoogle Scholar
  3. 3.
    W3c, QoS for Web Services: Requirements and Possible Approaches. W3C Working Group Note (November 2003)Google Scholar
  4. 4.
    Saaty, T.L.: How to make a decision: The analytic hierarchy process. European J. of Operational Research 48(1), 9–26 (1990)CrossRefzbMATHGoogle Scholar
  5. 5.
    Limam, N., Boutaba, R.: Assessing Software Service Quality and Trustworthiness at Selection Time. IEEE Trans. on Software Engineering 36(4), 559–574 (2010)CrossRefGoogle Scholar
  6. 6.
    Nocedal, J., Wright, S.J.: Numerical Optimization, 2nd edn. Springer Series in Operational Research (2006)Google Scholar
  7. 7.
    Rosario, S., Benveniste, A., Jard, C.: A Theory of QoS for Web Service Orchestrations. HAL INRIA Research Report (2009)Google Scholar
  8. 8.
    Saaty, T.L.: Decision-making with the AHP: Why is the principal eigenvector necessary. Elsevier European J. of Operational Research 145, 85–91 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Kapunscinski, R., Zhang, R.Q., Carbonneau, P., Moore, R., Reeves, B.: Inventory Decisions in Dell’s Supply Chain. Interfaces 34(3), 191–205 (2004)CrossRefGoogle Scholar
  10. 10.
    Rardin, R.L.: Optimization in Operations Research. Prentice Hall (1998)Google Scholar
  11. 11.
    Misra, J., Cook, W.R.: Computation Orchestration: A Basis for Wide-area Computing. J. of Software and Systems Modeling 6(1), 83–110 (2007)CrossRefGoogle Scholar
  12. 12.
    Fourer, R., Ma, J., Martin, K.: Optimization Services: A Framework for Distributed Optimization. COIN-OR (2008)Google Scholar
  13. 13.
    Fourer, R., Goux, J.: Optimization as an Internet Resource. Interfaces 31(2), 130–150 (2001)CrossRefGoogle Scholar
  14. 14.
    Alrifai, M., Risse, T.: Combining Global Optimization with Local Selection for Efficient QoS-aware Service Composition. In: Intl. World Wide Web Conf., Spain (2009)Google Scholar
  15. 15.
    Canfora, G., Di Penta, M., Esposito, R., Villani, M.L.: An approach for QoS-aware service composition based on genetic algorithms. In: Conf. on Genetic and Evolutionary Computation, USA, pp. 1069–1075 (2005)Google Scholar
  16. 16.
    Zeng, L., Benatallah, B., Ngu, A.H., Dumas, M., Kalagnanam, J., Chang, H.: QoS-aware middleware for Web services composition. IEEE Trans. on Software Engineering 30(5), 311–327 (2004)CrossRefGoogle Scholar
  17. 17.
    Yu, T., Zhang, Y., Lin, K.: Efficient Algorithms for Web Services Selection with End-to-End QoS Constraints. ACM Trans. on the Web 1(1) (2007)Google Scholar
  18. 18.
    Ho, W.: Integrated analytic hierarchy process and its applications A literature review. European J. of Operational Research 186(1), 211–228 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Cappiello, C., Comuzzi, M., Plebani, P.: On Automated Generation of Web Service Level Agreements. In: Krogstie, J., Opdahl, A.L., Sindre, G. (eds.) CAiSE 2007 and WES 2007. LNCS, vol. 4495, pp. 264–278. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  20. 20.
    Seo, Y.-J., Jeong, H.-Y., Song, Y.-J.: Best Web Service Selection Based on the Decision Making Between QoS Criteria of Service. In: Yang, L.T., Zhou, X.-s., Zhao, W., Wu, Z., Zhu, Y., Lin, M. (eds.) ICESS 2005. LNCS, vol. 3820, pp. 408–419. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ajay Kattepur
    • 1
  • Albert Benveniste
    • 1
  • Claude Jard
    • 2
  1. 1.IRISA/INRIA, Campus Universitaire de BeaulieuRennesFrance
  2. 2.ENS Cachan, IRISA, Université Européenne de BretagneBruzFrance

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