Advertisement

Dynamic Decentralized Packet Clustering in Networks

  • Daniel Merkle
  • Martin Middendorf
  • Alexander Scheidler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3449)

Abstract

In this paper we study a dynamic version of the Decentralized Packet Clustering (DPC) Problem. For a network consisting of routers and application servers the DPC problem is to find a good clustering for packets that are sent between the servers through the network. The clustering is done according to a data vector in the packets. In the dynamic version of DPC the packets data vector can change. The proposed algorithms to solve the dynamic DPC are inspired by the odor recognition system of ants. We analyze the new algorithms for situations with different strengths of dynamic change and for different number of routers in the network.

Keywords

Data Vector Test Instance Cluster Number Dynamic Version Dynamic Situation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Handl, J., Knowles, J., Dorigo, M.: Strategies for the increased robustness of ant-based clustering. In: Di Marzo Serugendo, G., Karageorgos, A., Rana, O.F., Zambonelli, F. (eds.) ESOA 2003. LNCS (LNAI), vol. 2977, pp. 90–104. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Handl, J., Knowles, J., Dorigo, M.: On the performance of ant-based clustering. In: Proc. 3rd Int. Conf. on Hybrid Intell. Systems (HIS 2003). IOS Press, Amsterdam (2003)Google Scholar
  3. 3.
    Kaufman, L., Rousseuw, P.J.: Finding Groups in Data: An Introduction to ClusterAnalysis. Wiley, New York (1990)CrossRefGoogle Scholar
  4. 4.
    Labroche, N., Monmarché, N., Venturini, G.: A new clustering algorithm based on the chemical recognition system of ants. In: Proc. Eur. Conf. on AI, pp. 345–349 (2002)Google Scholar
  5. 5.
    Labroche, N., Monmarché, N., Venturini, G.: AntClust: Ant Clustering and Web Usage Mining. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 25–36. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Labroche, N., Monmarché, N., Venturini, G.: Visual clustering with artificial ants colonies. In: Palade, V., Howlett, R.J., Jain, L. (eds.) KES 2003. LNCS, vol. 2773, pp. 332–338. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Lambertsen, G., Nishio, N.: Dynamic Clustering Techniques in Sensor Networks. In: Proc. 7th JSSST SIGSYS Workshop on Systems for Programming and Applications (SPA 2004) (2004)Google Scholar
  8. 8.
    Yang, J.: Dynamic Clustering of Evolving Streams with a Single Pass. In: Proc. 19th International Conference on Data Engineering (2003)Google Scholar
  9. 9.
    Merkle, D., Middendorf, M., Scheidler, A.: Decentralized Packet Clustering in Router-based Networks. International Journal of Foundations of Computer Science (2005) (to appear)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Daniel Merkle
    • 1
  • Martin Middendorf
    • 1
  • Alexander Scheidler
    • 1
  1. 1.Department of Computer ScienceUniversity of LeipzigLeipzigGermany

Personalised recommendations