Distributed Fault Location in Networks Using Learning Mobile Agents

  • Tony White
  • Bernard Pagurek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1733)


This paper describes how multiple interacting swarms of adaptive mobile agents can be used to locate faults in networks. The paper proposes the use of distributed problem solving using learning mobile agents for fault finding. The paper uses a recently described architectural description for an agent that is biologically inspired and proposes chemical interaction as the principal mechanism for inter-swarm communication. Agents have behavior that is inspired by the foraging activities of ants, with each agent capable of simple actions; global knowledge is not assumed. The creation of chemical trails is proposed as the primary mechanism used in distributed problem solving arising from the self-organization of swarms of agents. Fault location is achieved as a consequence of agents moving through the network, sensing, acting upon sensed information, and subsequently modifying the chemical environment that they inhabit. Elements of a mobile code framework that is being used to support this research, and the mechanisms used for agent mobility within the network environment, are described.


Mobile Agent Fault Location Network Management Problem Agent Swarm Intelligence 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Case, J. D., Fedor, M., Schoffstall, M. L. and Davin, C., Simple Network Management Protocol, RFC 1157, May 1990.Google Scholar
  2. 2.
    Yemini, Y., The OSI Network Management Model, IEEE Communication Magazine, pages 20–29, May 1993.Google Scholar
  3. 3.
    Yemini, Y., Goldszmidt, G. and Yemini, S., Network Management by Delegation. In The Second International Symposium on Integrated Network Management, Washington, DC, April 1991.Google Scholar
  4. 4.
    Case, J.D., and Levi, D. B., SNMP Mid-Level-Manager MIB, Draft, IETF, 1993.Google Scholar
  5. 5.
    Kotay, K. and Kotz, D., Transportable Agents. In Yannis Labrou and Tim Finin, editors, Proceedings of the CIKM Workshop on Intelligent Information Agents, Third International Conference on Information and Knowledge Management (CIKM 94), Gaithersburg, Maryland, December 1994.Google Scholar
  6. 6.
    Beckers R., Deneuborg J.L. and Goss S., Trails and U-turns in the Selection of a Path of the Ant Lasius Niger. In J. theor. Biol. Vol. 159, pp. 397–415.Google Scholar
  7. 7.
    Susilo, G., Bieszczad, A. and Pagurek, B., Infrastructure for Advanced Network Management based on Mobile Code, Proceedings IEEE/IFIP Network Operations and Management Symposium NOMS’ 98, New Orleans, Luisiana, February 1998.Google Scholar
  8. 8.
    Bieszczad, A. and Pagurek, B., Network Management Application-Oriented Taxonomy of Mobile Code, to be presented at the IEEE/IFIP Network Operations and Management Symposium NOMS’98, New Orleans, Louisiana, February 1998.Google Scholar
  9. 9.
    OMG, Mobile Agent Facility Specification, OMG TC cf/xx-x-xx, 2 June 1997.Google Scholar
  10. 10.
    Beni G., and Wang J., Swarm Intelligence in Cellular Robotic Systems, Proceedings of the NATO Advanced Workshop on Robots and Biological Systems, Il Ciocco, Tuscany, Italy.Google Scholar
  11. 11.
    Grassè P.P., La reconstruction du nid et les coordinations inter-individuelles chez Bellicoitermes natalenis et Cubitermes sp. La theorie de la stigmergie: Essai d’interpretation des termites constructeurs. In Insect Societies, Vol. 6, pp. 41–83.Google Scholar
  12. 12.
    Dorigo M., V. Maniezzo and A. Colorni, The Ant System: An Autocatalytic Optimizing Process. Technical Report No. 91-016, Politecnico di Milano, Italy.Google Scholar
  13. 13.
    White T., Routing and Swarm Intelligence, Technical Report SCE-97-15, Systems and Computer Engineering, Carleton University, September 1997.Google Scholar
  14. 14.
    Schoonderwoerd R., O. Holland and J. Bruten, Ant-like Agents for Load Balancing in Telecommunications Networks. Proceedings of Agents’ 97, Marina del Rey, CA, ACM Press pp. 209–216, 1997.Google Scholar
  15. 15.
    Di Caro G. and Dorigo M., AntNet: A Mobile Agents Approach to Adaptive Routing. Tech. Rep. IRIDIA/97-12, Universitè Libre de Bruxelles, Belgium, 1997.Google Scholar
  16. 16.
    Pagurek B., Li Y., Bieszczad A., and Susilo G., Configuration Management In Heterogeneous ATM Environments using Mobile Agents, Proceedings of the Second International Workshop on Intelligent Agents in Telecommunications Applications (IATA’ 98).Google Scholar
  17. 17.
    White T., Pagurek B. and Oppacher F., ASGA: Improving the Ant System by Integration with Genetic Algorithms. In Proceedings of the Third Genetic Programming Conference (SGA’ 98), July, 1998, pp. 610–617.Google Scholar
  18. 18.
    White T., Pagurek B., and Oppacher, F., Connection Management using Adaptive Agents. In Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA’98), July 12th-16th, 1998, pp. 802–809.Google Scholar
  19. 19.
    Goldberg, D., Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley, 1989.Google Scholar
  20. 20.
    Holland, J. H., Escaping Brittleness: the Possibilities of General-Purpose Learning Algorithms applied to Parallel Rule-Based Systems. In Machine Learning, an Artificial Intelligence Approach, Volume II, edited by R.S. Michalski, J.G. Carbonell and T.M. Mitchell, Morgan Kaufmann, 1986.Google Scholar
  21. 21.
    White T., and Pagurek B., Towards Multi-Swarm Problem Solving in Networks, Proceedings of the 3rd International Conference on Multi-Agent Systems (ICMAS’ 98), July 1998.Google Scholar
  22. 22.
    White T. and Pagurek B., Emergent Behaviour and Mobile Agents. In Proceedings of the Workshop on Mobile Agents in Coordination and Cooperation at Autonomous Agents’ 99, Seattle, May 1st-5th, 1999.Google Scholar
  23. 23.
    Ishida, Y., Active Diagnosis by Immunity-Based Agent Approach, Proceedings of the Seventh International Workshop on Principles of Diagnosis (DX 96), Val-Morin, Canada, pp. 106–114, 1996.Google Scholar
  24. 24.
    G. Berry and G. Boudol, The Chemical Abstract Machine, Theoretical Computer Science, 96(1), pp. 217–248, 1992.CrossRefMathSciNetGoogle Scholar
  25. 25.
    P. Maes, A Spreading Activation Network for Action Selection, Intelligent Autonomous Systems-2 Conference, Amsterdam, December 1989.Google Scholar
  26. 26.
    White, T., and Bieszczad, A., The Expert Advisor: An Expert System for Real Time Network Monitoring, European Conference on Artificial Intelligence, Proceedings of the Workshop on Advances in Real Time Expert Systems Technology, August, 1992.Google Scholar
  27. 27.
    White T., and Ross, N., Fault Diagnosis and Network Entities in a Next Generation Network Management System, in Proceedings of EXPERSYS-96, Paris, France, pp. 517–522.Google Scholar
  28. 28.
    White T. and Ross N., An Architecture for an Alarm Correlation Engine, Object Technology 97, Oxford, 13-16 April, 1997.Google Scholar
  29. 29.
    Wolpert D. H., Wheeler K. R. and Tumer K., General Principles of Learning-based Multi-Agent Systems. In Proceedings of the 3rd Annual Conference on Autonomous Agents, Seattle, pp. 77–83, May, 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Tony White
    • 1
  • Bernard Pagurek
    • 1
  1. 1.Systems and Computer EngineeringCarleton UniversityOttawaCanada K1S 5B6

Personalised recommendations