Towards a Biologically-inspired Architecture for Self-Regulatory and Evolvable Network Applications

  • Chonho Lee
  • Hiroshi Wada
  • Junichi Suzuki
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 69)

The BEYOND architecture applies biological principles and mechanisms to design network applications that autonomously adapt to dynamic environmental changes in the network. In BEYOND, each network application consists of distributed software agents, analogous to a bee colony (application) consisting of multiple bees (agents). Each agent provides a particular functionality of a network application, and implements biological behaviors such as energy exchange, migration, reproduction and replication. This paper describes two key components in BEYOND: (1) a self-regulatory and evolutionary adaptation mechanism for agents, called iNet, and (2) an agent development environment, called BEYONDwork. iNet is designed after the mechanisms behind how the immune system detects antigens (e.g., viruses) and produces antibodies to eliminate them. It models a set of environment conditions (e.g., network traffic) as an antigen and an agent behavior (e.g., migration) as an antibody. iNet allows each agent to autonomously sense its surrounding environment conditions (i.e., antigens) and adaptively invoke a behavior (i.e., antibody) suitable for the conditions. In iNet, a configuration of antibodies is encoded as a gene. Agents evolve their antibodies so that they can adapt to unexpected environmental changes. iNet also allows each agent to detect its own deficiencies to detect antigen invasions (i.e., environmental changes) and regulate its policy for antigen detection. Simulation results show that agents adapt to changing network environments by self-regulating their antigen detection and evolving their antibodies through generations. BEYONDwork provides visual and textual languages to design agents in an intuitive manner.

Keywords

Danger Signal User Request Network Application System Biology Markup Language Textual Language 
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.
    D. F. Carr, “How google works,” Baseline, July 2006.Google Scholar
  2. 2.
    J. Markoff and S. Hansell, “Google’s not-so-very-secret weapon,” International Herald Tribune, June 2006.Google Scholar
  3. 3.
    P. Dini, W. Gentzsch, M. Potts, A. Clemm, M. Yousif, and A. Polze, “Internet, grid, self-adaptability and beyond: Are we ready?” Proc. of IEEE International Workshop on Self-Adaptable and Autonomic Computing Systems, August 2006.Google Scholar
  4. 4.
    R. Sterritt and D. Bustard, “Towards an autonomic computing environment,” Proc. of IEEE International Workshop on Database and Expert Systems Applications, September 2003.Google Scholar
  5. 5.
    J. Rolia, S. Singhal, and R. Friedrich, “Adaptive internet data centers,” Proc. of International Conference on Advances in Infrastructure for Electronic Business, Science, and Education on the Internet, July 2000.Google Scholar
  6. 6.
    S. Ranjan, J. Rolia, E. Knightly, and H. Fu, “Qos-driven server migration for internet data centers,” Proc. of International Workshop on Quality of Service, May 2002.Google Scholar
  7. 7.
    N. Minar, K. H. Kramer, and P. Maes, “Cooperating mobile agents for dynamic network routing,” in Software Agents for Future Communications Systems, A. Hayzelden and J. Bigham, Eds. Springer, June 1999.Google Scholar
  8. 8.
    R. Albert, H. Jeong, and A. Barabasi, “Error and attack tolerance of complex networks,” Nature, July 2001.Google Scholar
  9. 9.
    G. Cabri, L. Leonardi, and F. Zambonelli, “Mobile-agent coordination models for internet applications,” IEEE Computer, February 2000.Google Scholar
  10. 10.
    S. Camazin, J. L. Deneubourg, N. R. Franks, J. Sneyd, G. Theraula, and E. Bonabeau, Self Organization in Biological Systems. Princeton University Press, May 2003.Google Scholar
  11. 11.
    N. K. Jerne, “Idiotypic networks and other preconceived ideas,” Immunological Review, 1984.Google Scholar
  12. 12.
    C. Berek, “Somatic hypermutation and b-cell receptor selection as regulators of the immune response,” Transfusion Medicine and Hemotherapy, 2005.Google Scholar
  13. 13.
    P. Matzinger, “The danger model: A renewed sense of self,” Science, April 2002.Google Scholar
  14. 14.
    K. R. Jerome and L. Corey, “The danger within,” The New England Journal of Medicine, 2004.Google Scholar
  15. 15.
    W. R. Heath and F. R. Carbonel, “Immunology: Dangerous liaisons,” Nature, October 2003.Google Scholar
  16. 16.
    B. Goldman, “White paper: Heat shock proteins’ vaccine potential from basic science breakthroughs to feasible personalized medicine,” Antigenic, July 2002.Google Scholar
  17. 17.
    W. A. Fenton and A. L. Horwich, “Chaperonin-mediated protein folding: Fate of substrate polypeptide,” Quarterly Reviews of Biophysics, May 2003.Google Scholar
  18. 18.
    T. Mitchell, Machine Learning. McGraw-Hill, 1997.Google Scholar
  19. 19.
    C. Lee and J. Suzuki, “Biologically-inspired design of autonomous and adaptive grid services,” Proc. of International Conference on Autonomic and Autonomous Systems, July 2006.Google Scholar
  20. 20.
    J. Chase, D. Anderson, P. Thakar, A. Vahdat, and R. Doyle, “Managing energy and server resources in hosting centers,” Proc. of the Eighteenth Symposium on Operating Systems Principles, October 2001.Google Scholar
  21. 21.
    C. Lee and J. Suzuki, “An immunologically-inspired adaptation mechanism for evolvable network applications,” Proc. of the Fourth IEEE Consumer Communications and Networking Conference, January 2007.Google Scholar
  22. 22.
    T. Nakano and T. Suda, “Self-organizing network services with evolutionary adaptation,” IEEE Transactions on Neural Networks, September 2005.Google Scholar
  23. 23.
    F. A. Gonzalez and D. Dasgupta, “Anomaly detection using real-valued negative selection,” Genetic Programming and Evolvable Machines, 2003.Google Scholar
  24. 24.
    L. N. de Castro and J. I. Timmis, “Artificial immune systems: A novel paradigm to pattern recognition,” in Artificial Neural Networks in Pattern Recognition, J. M. Corchado, L. Alonso, and C. Fyfe, Eds. University of Paisley, UK, 2002. Google Scholar
  25. 25.
    S. Sarafijanovic and J.-Y. L. Boudec, “An artificial immune system approach with secondary response for misbehavior detection in mobile adhoc networks,” IEEE Transactions on Neural Networks, Special Issue on Adaptive Learning Systems in Communication Network, April 2005.Google Scholar
  26. 26.
    G. Cook, “Domain-specific modeling and model-driven architecture,” in The MDA journal: Model Driven Architecture Straight from the Masters. Meghan-Kiffer Press, December 2004, ch. 5.Google Scholar
  27. 27.
    M. Hucka, A. Finney, B. Bornstein, S. Keating, B. Shapiro, J. Matthews, B. Kovitz, M. Schilstra, A. Funahashi, J. Doyle, and H. Kitano, “Evolving a lingua franca and associated software infrastructure for computational systems biology: The systems biology markup language (sbml) project,” Systems Biology Journal, June 2004.Google Scholar
  28. 28.
    F. Kolpakov, “Biouml - framework for visual modeling and simulation biological systems,” in International Conference on Bioinformatics of Genome Regulation and Struc-ture, July 2002.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Chonho Lee
    • 1
  • Hiroshi Wada
    • 1
  • Junichi Suzuki
    • 1
  1. 1.Department of Computer ScienceUniversity of MassachusettsBostonUSA

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