Model Driven Provisioning: Bridging the Gap Between Declarative Object Models and Procedural Provisioning Tools

  • Kaoutar El Maghraoui
  • Alok Meghranjani
  • Tamar Eilam
  • Michael Kalantar
  • Alexander V. Konstantinou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4290)


Today’s enterprise data centers support thousands of mission-critical business applications composed of multiple distributed heterogeneous components. Application components exhibit complex dependencies on the configuration of multiple data center network, middleware, and related application resources. Applications are also associated with extended life-cycles, migrating from development to testing, staging and production environments, with frequent roll-backs. Maintaining end-to-end data center operational integrity and quality requires careful planning of (1) application deployment design, (2) resource selection, (3) provisioning operation selection, parameterization and ordering, and (4) provisioning operation execution. Current data center management products are focused on workflow-based automation of the deployment processes. Workflows are of limited value because they hard-code many aspects of the process, and are thus sensitive to topology changes. An emerging and promising class of model-based tools is providing new methods for designing detailed deployment topologies based on a set of requirements and constraints. In this paper we describe an approach to bridging the gap between generated “desired state” models and the elemental procedural provisioning operations supported by data center resources. In our approach, we represent the current and desired state of the data center using object models. We use AI planning to automatically generate workflows that bring the data center from its current state to the desired state. We discuss our optimizations to Partial Order Planning algorithms for the provisioning domain. We validated our approach by developing and integrating a prototype with a state of the art provisioning product. We also present initial results of a performance study.


Data Center Planning Algorithm Resource Selection Partial Plan Network Interface Card 
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.


  1. 1.
    Brown, A.B., Keller, A., Hellerstein, J.: A model of configuration complexity and its applications to a change management system. In: International Symposium on Integrated Network Management (2005)Google Scholar
  2. 2.
    IBM: Tivoli provisioning manager (2006),
  3. 3.
    Eilam, T., Kalantar, M., Konstantinou, A., Pacifici, G.: Reducing the complexity of application deployment in large data centers. In: International Symposium on Integrated Network Management (2005)Google Scholar
  4. 4.
    Eilam, T., Kalantar, M., Konstantinou, A., Pacifici, G., Pershing, J., Agrawal, A.: Managing the configuration complexity of distributed applications in internet data centers. IEEE Communication Magazine 44, 166–177 (2006)CrossRefGoogle Scholar
  5. 5.
    Felfernig, A., Friedrich, G.E., et al.: UML as a domain specific knowledge for the construction of knowledge based configuration systems. In: Ruhe, G., Bomarius, F. (eds.) SEKE 1999. LNCS, vol. 1756. Springer, Heidelberg (2000)Google Scholar
  6. 6.
    Taylor, R., Frank, R.: Codasyl data-base management systems. ACM Comput. Surv. 8, 67–103 (1976)CrossRefzbMATHGoogle Scholar
  7. 7.
    Ghallab, M., Howe, A., Knoblock, C., McDermott, D., Ram, A., Veloso, M., Weld, D., Wilkins, D.: PDDL—the planning domain definition language (1998)Google Scholar
  8. 8.
    Blum, A.L., Furst, M.L.: Fast planning through planning graph analysis. Artif. Intell. 90, 281–300 (1997)CrossRefzbMATHGoogle Scholar
  9. 9.
    Weld, D.S.: An introduction to least commitment planning. AI Magazine 15, 27–61 (1994)Google Scholar
  10. 10.
    Minton, S., Bresina, J.L., Drummond, M.: Total-order and partial-order planning: A comparative analysis. Journal of Artificial Intelligence Research 2, 227–262 (1994)Google Scholar
  11. 11.
    Knoblock, C.A., Yang, Q.: Relating the performance of partial-order planning algorithms to domain features. SIGART Bulletin 6, 8–15 (1995)CrossRefGoogle Scholar
  12. 12.
    McAllester, D., Rosenblitt, D.: Systematic nonlinear planning. In: Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI 1991), vol. 2, pp. 634–639. AAAI Press/MIT Press (1991)Google Scholar
  13. 13.
    Penberthy, J.S., Weld, D.S.: UCPOP: A sound, complete, partial order planner for ADL. In: Nebel, B., Rich, C., Swartout, W. (eds.) KR 1992. Principles of Knowledge Representation and Reasoning: Proc. of the 3rd Int. Conf., pp. 103–114. Morgan Kaufmann, San Mateo (1992)Google Scholar
  14. 14.
    Force, D.M.T.: Common Information Model (CIM) Standards (2006),
  15. 15.
    Arshad, N., Heimbigner, D., Wolf, A.L.: Deployment and dynamic reconfiguration planning for distributed software systems. In: 15th IEEE Int. Conf. on Tools with Artificial Intelligence, pp. 39–46. IEEE Press, Los Alamitos (2003)CrossRefGoogle Scholar
  16. 16.
    Keller, A., Hellerstein, J., Wolf, J., Wu, K., Krishnan, V.: The champs system: Change management with planning and scheduling. In: IEEE/IFIP Network Operations and Management Symposium (NOMS 2004). IEEE Press, Los Alamitos (2004)Google Scholar
  17. 17.
    Kichkaylo, T., Karamcheti, V.: Optimal resource-aware deployment planning for component-based distributed applications. In: HPDC 2004: 13th IEEE Int. Symp. on High Performance Distributed Computing (HPDC 2004), Washington, DC, USA, pp. 150–159. IEEE Computer Society, Los Alamitos (2004)Google Scholar
  18. 18.
    Su, X., Rao, J.: A survey of automated web service composition methods. In: Cardoso, J., Sheth, A.P. (eds.) SWSWPC 2004. LNCS, vol. 3387, pp. 43–54. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  19. 19.
    Blythe, J., Deelman, E., Gil, Y., Kesselman, C., Agarwal, A., Mehta, G., Vahi, K.: The role of planning in grid computing. In: 13th International Conference on Automated Planning and Scheduling (ICAPS), Trento, Italy (2003)Google Scholar
  20. 20.
    Deelman, E., Blythe, J., Gil, Y., Kesselman, C., Mehta, G., Vahi, K., Blackburn, K., Lazzarini, A., Arbree, A., Cavanaugh, R., Koranda, S.: Mapping abstract complex workflows onto grid environments. J. Grid Comput. 1, 25–39 (2003)CrossRefGoogle Scholar
  21. 21.
    Yang, J., Papazoglou, M.P., Orriëns, B., van den Heuvel, W.J.: A rule based approach to the service composition life-cycle. In: WISE, pp. 295–298. IEEE Computer Society, Los Alamitos (2003)Google Scholar
  22. 22.
    Kim, J., Spraragen, M., Gil, Y.: An intelligent assistant for interactive workflow composition. In: IUI 2004: 9th international conference on Intelligent user interface, pp. 125–131. ACM Press, New York (2004)Google Scholar
  23. 23.
    Wu, J., Sirin, E., Hendler, J., Nau, D., Parsia, B.: Automatic web services composition using shop2. In: Fensel, D., Sycara, K.P., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 195–210. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  24. 24.
    McIlraith, S., Son, T., Zeng, H.: Semantic web services (2001)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2006

Authors and Affiliations

  • Kaoutar El Maghraoui
    • 1
  • Alok Meghranjani
    • 2
  • Tamar Eilam
    • 3
  • Michael Kalantar
    • 3
  • Alexander V. Konstantinou
    • 3
  1. 1.Dept. of Computer ScienceRensselaer Polytechnic InstituteTroyUSA
  2. 2.École Polytechnique Fédérale de LausanneLausanneSwitzerland
  3. 3.IBM T.J. Watson Research CenterHawthorneUSA

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