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Deploying Distributed State Information in Mobile Agent Systems

  • Ralf-Dieter Schimkat
  • Michael Friedrich
  • Wolfgang Küchlin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2172)

Abstract

Agent-based distributed problem solving environments decompose a given problem into a set of sub problems which can be processed in parallel and independently by autonomous and mobile agents at each computation node. Such an autonomous agent primarily makes use of local information which is provided at the respective computation node. This kind of information is characterized by its potential incompleteness and inconsistency with regard to the overall distributed system state which is due to the lack of any centralized coordination facility. In this paper, we introduce the use of long-term knowledge repositories for autonomous agents without sacrifying the autonomy of the agents and without introducing any central management facility. We compare our approach to an agent-enabled distributed SAT prover which makes only use of local system state information. In that problem solving application a given search tree is distributed dynamically by autonomous mobile agents implemented in pure Java. To demonstrate the profit of using knowledge repositories in general, we integrated our agent system Okeanos into our XML-based monitoring system Specto and the lightweight, distributed event-based middleware Mitto. Our cooperative approach does not conflict with the decentralized parallelization algorithm of the distributed SAT prover. Empirical results show that our approach can contribute to the performance of distributed symbolic computation. In this example, a load balancing subsystem is implemented taking the now available global information about the system state appropriately into account.

Keywords

Mobile Agent Autonomous Agent Computation Node Tuple Space Knowledge Repository 
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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References

  1. 1.
    G. Cabri, L. Leonardi, and F. Zambonelli. XML dataspaces for mobile agent coordination. In Proceedings of the 2000 ACM Symposium on Applied Computing, Mar. 2000. 93Google Scholar
  2. 2.
    M. Davis and H. Putnam. A Computing Procedure for Quantification Theory. In Journal of the ACM, volume 7, pages 201–215, 1960. 86zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    T. Finn, Y. Labrou, and J. Mayfield. KQML as an Agent Communication Language. In J. Bradshaw, editor, Software Agents, pages 291–316. MIT Press, 1997. 82Google Scholar
  4. 4.
    E. Friedman-Hill. Jess, The Java Expert System Shell. Available at the URL: http://herzberg.ca.sandia.gov/jess/, 1999. 82
  5. 5.
    A. Fugetta, G. Picco, and G. Vigna. Understanding code mobility. IEEE Transactions on Software Engineering, 24(5):342–361, May 1998. 81CrossRefGoogle Scholar
  6. 6.
    M. Ghanea-Hercock, J. Collis, and D. Ndumu. Co-operating mobile agents for distributed parallel processing. In O. Etzioni, J. Müller, and J. Bradshaw, editors, Proceedings of the Third International Conference on Autonomous Agents (Agents’99). ACM Press, 1999. 93Google Scholar
  7. 7.
    R. Johnson and B. Foote. Designing Reusable Classes. Object-Oriented Programming, 1(2):22–35, 1988. 82Google Scholar
  8. 8.
    J. Joyce, G. Lomow, K. Slind, and B. Unger. Monitoring distributed systems. ACM Transactions on Computer Systems, 5(2):121–150, Feb. 1987. 83CrossRefGoogle Scholar
  9. 9.
    W. Küchlin and C. Sinz. Proving Consistency Assertions for Automotive Product Data Management. In I. P. Gent and T. Walsh, editors, Journal of Automated Reasoning. Kluwer Academic Publishers, 2000. 85, 86Google Scholar
  10. 10.
    R.-D. Schimkat, W. Blochinger, C. Sinz, M. Friedrich, and W. Küchlin. A servicebased agent framework for distributed symbolic computation. In M. Bubak, R. Williams, H. Afsarmanesh, and B. Hertzberger, editors, Proceedings of the 8th International Conference on High Performance Computing and Networking Europe (HPCN’00), volume 1823, pages 644–656, Amsterdam, Netherlands, May 2000. Springer-Verlag, Berlin. 82, 85, 88Google Scholar
  11. 11.
    R.-D. Schimkat, M. Häusser, W. Küchlin, and R. Krautter. Web application middleware to support XML-based monitoring in distributed systems. In N. Debnath, editor, Proceedings of 13th International Conference on Computer and Applications in Industry and Engineering (CAINE 2000), pages 203–207, Hawaii, USA, Nov. 2000. International Society for Computers and Their Applications. 82, 83Google Scholar
  12. 12.
    R.-D. Schimkat, S. Müller, W. Küchlin, and R. Krautter. A lightweight, messageoriented application server for the WWW. InJ. Carroll, E. Damiani, H. Haddad, and D. Oppenheim, editors, Proceedings of the 15th ACM Symposium on Applied Computing (SAC 2000), pages 934–941, Como, Italy, Mar. 2000. 82Google Scholar
  13. 13.
    R. T. Snodgrass. Relational approach to monitoring complex systems. ACM Transactions on Computer Systems, 6(2):157–196, 1988. 93CrossRefGoogle Scholar
  14. 14.
    J. Waldo, G. Wyant, A. Wollrath, and S. Kendall. A note on distributed computing. Technical Report TR-94-29, SUN Microsystems Laboratories, Nov 1994. 81Google Scholar
  15. 15.
    World Wide Web Consortium (W3C), http://www.w3.org/TR/REC-xml. Extensible Markup Language (XML) 1.0, Oct. 2000. 84

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Ralf-Dieter Schimkat
    • 1
  • Michael Friedrich
    • 1
  • Wolfgang Küchlin
    • 1
  1. 1.Symbolic Computation GroupUniversity of TübingenTübingenGermany

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