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Networks for networks: Internet analysis using graphical statistical models

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2 Author(s)
Coates, M. ; Dept. of Electr. & Comput. Eng., Rice Univ., Houston, TX, USA ; Nowak, R.

A novel graphical framework for statistical modeling of distributed computer networks is presented in this paper. The framework enables the inference of packet losses across internal links in the network based solely on external (end-to-end) measurements, which can be easily made at end systems without network cooperation. This inference problem is commonly referred to as network tomography. Our modeling and inference framework is based on probabilistic factor graphs (or Bayesian networks). A computationally efficient probability propagation (message passing) algorithm is developed for network inference that is capable of producing exact marginal distributions (as well as point estimates) of link-level network parameters. Simulation experiments demonstrate the potential of our new framework

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Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop  (Volume:2 )

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