Applying Blood Glucose Homeostatic Model Towards Self-management of IP QoS Provisioned Networks

  • Sasitharan Balasubramaniam
  • Dmitri Botvich
  • William Donnelly
  • Nazim Agoulmine
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4268)


Due to the rapid growth of the Internet architecture and the complexities required for network management, the need for efficient resource management is a tremendous challenge. This paper presents a biologically inspired self-management technique for IP Quality of Service (QoS) prov-isioned network using the blood glucose regulation model of the human body. The human body has the capability to maintain overall blood glucose level depending on the intensity of activity performed and at the same time produce the required energy based on the fitness capacity of the body. We have applied these biological principles to resource management, which includes (i) the ability to manage resources based on predefined demand profile as well as unexpected and fluctuating traffic, and (ii) the ability to efficiently manage multiple traffic types on various paths to ensure maximum revenue is obtained. Simulation results have also been presented to help validate our biologically inspired self-management technique.


Internet Service Provider Aerobic Respiration Anaerobic Respiration Primary Path Spare Capacity 


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  1. 1.
    Suzuki, J., Suda, T.: A Middleware Platform for a Biologically Inspired Network Architecture Supporting Autonomous and Adaptive Applications. IEEE Journal on Selected Areas in Communications 23(2) (February 2005)Google Scholar
  2. 2.
    Leibnitz, K., Wakamiya, N., Murata, M.: Biologically inspired Self-Adaptive Multi-path routing in overlay networks. Communications of the ACM 49(3) (March 2006)Google Scholar
  3. 3.
    Di Caro, G., Dorigo, M.: AntNet: Distributed stigmergetic control for communication networks. Journal of Artificial Intelligence Research 9, 317–365 (1998)MATHGoogle Scholar
  4. 4.
    Mantar, H.A., Hwang, J., Okumus, I.T., Chapin, S.: A Scalable Intra-Domain Resource Management Scheme for Diffserv Networks. In: Proceedings of 2nd International Working Conference on Performance modeling and evaluation of heterogeneous networks, West Yorkshire, UK (July 2004)Google Scholar
  5. 5.
    Gojmerac, I., Ziegler, T., Ricciato, F., Reichl, P.: Adaptive Multipath Routing for Dynamic Traffic Engineering. In: IEEE Globecom, San Francisco, USA (December 2003)Google Scholar
  6. 6.
    Yagan, D., Tham, C.-K.: Self-Optimizing Architecture for QoS Provisioning in Differentiated Services. In: Proceedings of the Second International Conference on Autonomic Computing (ICAC 2005), Seattle, WA, USA (June 2005)Google Scholar
  7. 7.
    Raven, P., Johnson, G., Losos, J., Singer, S.: Biology, 6th edn. McGraw-Hill, New York (2002)Google Scholar
  8. 8.
    Balasubramaniam, S., Donnelly, W., Botvich, D., Agoulmine, N., Strassner, J.: Towards integrating principles of Molecular Biology for Autonomic Network Management. In: Hewlett Packard Open View University Association (HPOVUA) conference, Nice, France (accepted) (May 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sasitharan Balasubramaniam
    • 1
  • Dmitri Botvich
    • 1
  • William Donnelly
    • 1
  • Nazim Agoulmine
    • 2
  1. 1.Telecommunications Software & Systems GroupWaterford Institute of TechnologyWaterfordIreland
  2. 2.Networks and Multimedia Systems GroupUniversity of Evry Val d’EssonneEvry CroucouronnesFrance

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