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Using Object-Oriented Constraint Satisfaction for Automated Configuration Generation

  • Tim Hinrichs
  • Nathaniel Love
  • Charles Petrie
  • Lyle Ramshaw
  • Akhil Sahai
  • Sharad Singhal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3278)

Abstract

In this paper, we describe an approach for automatically generating configurations for complex applications. Automated generation of system configurations is required to allow large-scale deployment of custom applications within utility computing environments. Our approach models the configuration management problem as an Object-Oriented Constraint Satisfaction Problem (OOCSP) that can be solved efficiently using a resolution-based theorem-prover. We outline the approach and discuss both the benefits of the approach as well as its limitations, and highlight certain unresolved issues that require further work. We demonstrate the viability of this approach using an e-Commerce site as an example, and provide results on the complexity and time required to solve for the configuration of such an application.

Keywords

Unify Modeling Language Theorem Prover Constraint Satisfaction Problem Class Definition Target Class 
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.
    Unified Modeling Language (UML), http://www.uml.org/
  2. 2.
  3. 3.
  4. 4.
    Sahai, Singhal, S., Joshi, R., Machiraju, V.: Automated Policy-Based Resource Construction in Utility Environments. In: Proceedings of the IEEE/IFIP NOMS, Seoul, Korea, April 19-23 (2004)Google Scholar
  5. 5.
    Paltrinieri, M.: Some Remarks on the Design of Constraint Satisfaction Problems. In: Second International Workshop on the Principles and Practice of Constraint Programming, pp. 299–311 (1994)Google Scholar
  6. 6.
  7. 7.
    Robinson, J.A.: A machine-oriented logic based on the resolution principle. Journal of the Association for Computing Machinery 12, 23–41 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Mittal, S., Araya, A.: A Knowledge-Based Framework for Design. In: Proceedings of the 5th AAAI (1986)Google Scholar
  9. 9.
    Petrie: Context Maintenance. In: Proceedings AAAI 1991, pp. 288–295 (1991)Google Scholar
  10. 10.
    Sahai, Singhal, S., Joshi, R., Machiraju, V.: Automated Generation of Resource Configurations through Policy. In: Proceedings of the IEEE 5th International Workshop on Policies for Distributed Systems and Networks, YorkTown Heights, NY, June 7-9 (2004) (to appear)Google Scholar
  11. 11.
    McCarthy, J., Hayes, P.J.: Some philosophical problems from the standpoint of artificial intelligence. Machine Intelligence 4, 463–502 (1969)zbMATHGoogle Scholar
  12. 12.
    Hall, M.R., Kumaran, K., Peak, M., Kaminski, J.S.: DESIGN: A Generic Configuration Shell. In: Proceedings 3rd International Conference on Industrial & Engineering Applications of AI and Expert Systems (1990)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2004

Authors and Affiliations

  • Tim Hinrichs
    • 1
  • Nathaniel Love
    • 1
  • Charles Petrie
    • 1
  • Lyle Ramshaw
    • 2
  • Akhil Sahai
    • 2
  • Sharad Singhal
    • 2
  1. 1.Stanford UniversityUSA
  2. 2.HP LaboratoriesPalo-AltoUSA

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