A Fuzzy Approach for Negotiating Quality of Services

  • Davide Bacciu
  • Alessio Botta
  • Hernán Melgratti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4661)

Abstract

A central point when integrating services concerns to the description, agreement and enforcement of the quality aspect of service interaction, usually known as Service Level Agreement (SLA). This paper presents a framework for SLA negotiation based on fuzzy sets. We propose (i) a request language for clients to describe quality preferences, (ii) a publication language for providers to define the qualities of their offered services, and (iii) a decision procedure for granting any client request with a SLA contract fitting the requestor requirements. We start with a restricted framework in which the different qualities of a service are handled independently (as being orthogonal) and then we propose an extension that allows clients and providers to express dependencies among different qualities.

Keywords

Service Level Service Level Agreement Linguistic Term Fuzzy Approach Acceptance Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Babuska, R.: Fuzzy systems, modeling and identification. Technical report, Delft University of Technology (2001)Google Scholar
  2. 2.
    Buscemi, M., Montanari, U.: CC-Pi: A constraint-based language for specifying service level agreements. Manuscript (2006)Google Scholar
  3. 3.
    Dubois, D., Prade, H.: Fuzzy Sets and Systems - Theory and Applications. Academic Press, New York (1980)MATHGoogle Scholar
  4. 4.
    Gorla, D., Hennessy, M., Sassone, V.: Security policies as membranes in systems for global computing. Logical Methods in Computer Science, 1(3) (2005)Google Scholar
  5. 5.
    Huang, C.-L., Chao, K.-M., Lo, C.-C.: A moderated fuzzy matchmaking for web services. In: CIT 2005: Proceedings fo the Fifth International Conference on Computer and Information Technology, pp. 1116–1122. IEEE Computer Society, Los Alamitos (2005)CrossRefGoogle Scholar
  6. 6.
    Keller, A., Ludwig, H.: The WSLA framework: Specifying and monitoring service level agreements for web services. Journal of Network and Systems Management, 11(1) (2003)Google Scholar
  7. 7.
    Lin, M., Xie, J., Guo, H., Wang, H.: Solving qos-driven web service dynamic composition as fuzzy constraint satisfaction. In: EEE 2005: Proceedings of the 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE 2005) on e-Technology, e-Commerce and e-Service, pp. 9–14. IEEE Computer Society, Washington, DC, USA (2005)Google Scholar
  8. 8.
    Mohri, M.: Semiring frameworks and algorithms for shortest-distance problems. Journal of Automata Languages and Combinatorics 7(3), 321–350 (2002)MATHMathSciNetGoogle Scholar
  9. 9.
    Mohri, M.: Weighted finite-state transducer algorithms: An overview. In: Martín-Vide, C., Mitrana, V., Paun, G. (eds.) Formal Languages and Applications. LNCS, vol. 148, Springer, Heidelberg (2004)Google Scholar
  10. 10.
    De Nicola, R., Ferrari, G., Montanari, U., Pugliese, R., Tuosto, E.: A formal basis for reasoning on programmable qos. In: Dershowitz, N. (ed.) Verification: Theory and Practice. LNCS, vol. 2772, pp. 436–479. Springer, Heidelberg (2003)Google Scholar
  11. 11.
    Di Penta, M., Troiano, L.: Using fuzzy logic to relax constraints in GA-based service composition. In: GECCO 2005: Proceedings of the 2005 Genetic and Evolutionary Computation Conference (2005)Google Scholar
  12. 12.
    Setnes, M., Babuska, R., Kaymak, U., van Nauta Lemke, H.R.: Similarity measures in fuzzy rule base simplification. IEEE Transactions on Systems, Man, and Cybernetics, Part B 28(3), 376–386 (1998)CrossRefGoogle Scholar
  13. 13.
    Web services agreement specification (ws-agreement). version 2005/09 (2005)Google Scholar
  14. 14.
    Zadeh, L.A.: Fuzzy Sets. Information and Control 3(8), 338–353 (1965)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Zimmermann, H.-J.: Fuzzy set theory and its applications, 3rd edn. Kluwer Academic Publishers, Norwell, MA, USA (1996)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Davide Bacciu
    • 1
  • Alessio Botta
    • 1
  • Hernán Melgratti
    • 1
  1. 1.IMT Lucca Institute for Advanced StudiesItaly

Personalised recommendations