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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)

Abstract

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.

Keywords

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.

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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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