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Service Composition with Pareto-Optimality of Time-Dependent QoS Attributes

  • Benjamin Klöpper
  • Fuyuki Ishikawa
  • Shinichi Honiden
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6470)

Abstract

Quality of Services (QoS) plays an essential role in realizing user tasks by service composition. Most QoS-aware service composition approaches have ignored the fact that QoS values can depend on the time of execution. Common QoS attributes such as response time may depend for instance on daytime, due to access tendency or conditional Service of Level Agreements. Application-specific QoS attributes often have tight relationships with the current state of resources, such as availability of hotel rooms. In response to these problems, this paper proposes an integrated multi-objective approach to QoS-aware service composition and selection.

Keywords

Service Composition Multi-objective Optimization 

References

  1. 1.
    Argarwal, V., Chafle, G., Mittal, S., Srivastava, B.: Understanding Approaches for Web Service Composition and Execution. In: Proceedings of the 1st Bangalore Annual Compute Conference, ACN (2008)Google Scholar
  2. 2.
    Chen, K., Xu, J., Reiff-Marganiec, S.: Markov-HTN Planning Approach to Enhance Flexibility of Automatic Web Service Composition. In: IEEE International Conference on Web Services, pp. 9–16 (2009)Google Scholar
  3. 3.
    Kalasupur, S., Kumar, M., Behrooz, A.: Dynamic Service Composition in Pervasive Computing. IEEE Transactions on Parallel and Distributed System 18, 907–917 (2007)CrossRefGoogle Scholar
  4. 4.
    Klöpper, B.: First Steps Towards Distributed Multiobjective Scheduling for Self-Optimizing Manufacturing Systems. In: 10th IFAC Workshop on Intelligent Manufacturing Systems (2010)Google Scholar
  5. 5.
    Mandow, L., Pérez de la Cruz, J. L.: A new approach to multiobjective A* Search. In: Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI 2005). pp. 218–223. Edinburgh, Scotland (2005)Google Scholar
  6. 6.
    Stewart, B.S., White, C.C.: Multiobjective A*. Journal of the Association for Computing Machinery 38(4), 775–814 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Wang, J., Hou, Y.: Optimal Web Service Selection based on Multi-Objective Genetic Algorithm. In: International Symposium on Computational Intelligence and Design (2008)Google Scholar
  8. 8.
    Zeng, L., Benatallah, B., Ngu., A., Dumas, M., Kalagnanam, Chang, H.: QoS-Aware Middleware for Web Services Composition. IEEE Transactions on Software Engineering 30, 311–327 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Benjamin Klöpper
    • 1
  • Fuyuki Ishikawa
    • 1
  • Shinichi Honiden
    • 1
  1. 1.National Institute of InformaticsChiyoda-kuJapan

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