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Automatic Realization of SOA Deployment Patterns in Distributed Environments

  • William Arnold
  • Tamar Eilam
  • Michael Kalantar
  • Alexander V. Konstantinou
  • Alexander A. Totok
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5364)

Abstract

Deployment patterns have been proposed as a mechanism to support the provisioning of SOA-based services. Deployment patterns represent the structure and constraints of composite solutions, including non-functional properties, such as performance, availability, and security, without binding to specific resource instances. In previous work [1], we have presented a formal mechanism for capturing such service deployment patterns using models. Our pattern models define abstract connectivity and configuration requirements which are then realized by an existing or planned infrastructure. Realization mapping is used to enforce policies, and is materialized at deployment time. In this paper we extend that work to address the problem of automatic pattern realization over a given infrastructure. We first formalize the problem and present three variations of increasing power and complexity. We then present a variation of a search-based graph isomorphism algorithm with extensions for our pattern model semantics. Next, we show that our worst-case exponential complexity algorithm performs well in practice, over a number of pattern and infrastructure combinations. We speculate that this is because deployment topologies represent heavily labeled and sparse graphs. We present a number of heuristics which we have compared experimentally, and have identified one which performs best across most scenarios. Our algorithm has been incorporated into a large deployment modeling platform, now part of the IBM Rational Software Architect (RSA) tool [2].

Keywords

Graph Isomorphism Pattern Realization Pattern Topology Service Deployment Target Unit 
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.

References

  1. 1.
    Arnold, W., Eilam, T., Kalantar, M., Konstantinou, A., Totok, A.: Pattern based SOA deployment. In: Krämer, B.J., Lin, K.-J., Narasimhan, P. (eds.) ICSOC 2007. LNCS, vol. 4749, pp. 1–12. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    IBM: Rational Software Architect for WebSphere Software (RSA) V7.5 (September 2008)Google Scholar
  3. 3.
    Mehra, P.: Global deployment of data centers. IEEE Internet Computing 6(5) (September 2002)Google Scholar
  4. 4.
    Brown, A.B., Keller, A., Hellerstein, J.: A model of configuration complexity and its applications to a change management system. In: Integrated Management (2005)Google Scholar
  5. 5.
    Bossardt, M., Mühlemann, A., Zürcher, R., Plattner, B.: Pattern based service deployment for active networks. In: ANTA (2003)Google Scholar
  6. 6.
    Ludwig, H., Gimpel, H., Dan, A., Kearney, B.: Template based automated service provisioning supporting the agreement driven service life-cycle. In: Benatallah, B., Casati, F., Traverso, P. (eds.) ICSOC 2005. LNCS, vol. 3826, pp. 283–295. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Redlin, C., Carlson-Neumann, K.: WebSphere Process Server and WebSphere Enterprise Service Bus deployment patterns. Technical report, IBM (November 2006)Google Scholar
  8. 8.
    IBM: WebSphere Process Server (WPS) V6.1 (2007)Google Scholar
  9. 9.
    Garey, M., Johnson, D.: Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman and Company, New York (1979)zbMATHGoogle Scholar
  10. 10.
    Messmer, B.T.: Efficient Graph Matching Algorithms. PhD thesis, University of Bern, Switzerland (November 1995)Google Scholar
  11. 11.
    IBM: Tivoli Provisioning Manager, TPM (2006)Google Scholar
  12. 12.
    El Maghraoui, K., Meghranjani, A., Eilam, T., Kalantar, M., Konstantinou, A.: Model driven provisioning: Bridging the gap between declarative object models and procedural provisioning tools. In: van Steen, M., Henning, M. (eds.) Middleware 2006. LNCS, vol. 4290, pp. 404–423. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Keller, A., Hellerstein, J., Wolf, J., Wu, K.L., Krishnan, V.: The CHAMPS system: change management with planning and scheduling. In: NOMS. IEEE Press, Los Alamitos (2004)Google Scholar
  14. 14.
    Corneil, D., Gotlieb, C.: An efficient algorithm for graph isomorphism. Journal of the ACM 17, 51–64 (1970)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Ullman, J.: An algorithm for subgraph isomorphism. Journal of the ACM 23(1), 31–42 (1976)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Gati, G.: Further annotated bibliography on the isomorphism disease. Journal of Graph Theory, 96–109 (1979)Google Scholar
  17. 17.
    Kitchen, L., Rosenfeld, A.: Discrete relaxation for matching relational structures. IEEE Transactions on Systems, Man, and Cybernetics 9(12), 869–874 (1979)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Haralick, R., Elliot, G.: Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence 14, 263–313 (1980)CrossRefGoogle Scholar
  19. 19.
    Hoffman, C.: Group-Theoretic Algorithms and Graph Isomorphism. Springer, Heidelberg (1982)CrossRefGoogle Scholar
  20. 20.
    Kim, W., Kak, A.: 3-D object recognition using bipartite matching embedded in discrete relaxation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 13, 224–251 (1991)CrossRefGoogle Scholar
  21. 21.
    Tsai, W., Fu, K.: Error-correcting isomorphisms of attributed relational graphs for pattern recognition. IEEE Trans. on Sys., Man, and Cybernetics 9, 757–768 (1979)CrossRefzbMATHGoogle Scholar
  22. 22.
    De Jong, K., Spears, W.: Using genetic algorithms to solve NP-Complete problems. In: Schaffer, J.D. (ed.) Genetic Algorithms, pp. 124–132. Morgan Kaufmann, San Francisco (1989)Google Scholar
  23. 23.
    Abrams, S., Bloom, B., Keyser, P., Kimelman, D., Nelson, E., Neuberger, W., Roth, T., Simmonds, I., Tang, S., Vlissides, J.: Architectural thinking and modeling with the Architects’ Workbench. IBM Systems Journal 45(3) (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • William Arnold
    • 1
  • Tamar Eilam
    • 1
  • Michael Kalantar
    • 1
  • Alexander V. Konstantinou
    • 1
  • Alexander A. Totok
    • 1
  1. 1.IBM T.J. Watson Research Center, HawthorneNYUSA

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