Network topology discovery using finite mixture models (2004)
| Venue: | IEEE Int’l Conf. on Acoustics, Speech, and Signal Processing (ICASSP) 2003 |
| Citations: | 8 - 2 self |
BibTeX
@INPROCEEDINGS{Shih04networktopology,
author = {Meng-fu Shih and Alfred O. Hero Iii},
title = {Network topology discovery using finite mixture models},
booktitle = {IEEE Int’l Conf. on Acoustics, Speech, and Signal Processing (ICASSP) 2003},
year = {2004}
}
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Abstract
In this article we propose a network topology estimation strategy using unicast end-to-end packet pair delay measurements that is based on mixture models for the delay covariances. An unsupervised learning algorithms is applied to estimate the number of mixture components and delay covariances. The leaf pairs are clustered by a MAP criterion and passed to a hierarchical topology construction algorithm to rebuild the tree. Results from an ns simulation show that our algorithm can identify a network tree with 8 leaf nodes. 1.
Citations
| 201 | Unsupervised learning of finite mixture models - Figueiredo, Jain - 2002 |
| 58 | Network loss inference using unicast end-to-end measurement - Coates, Nowak - 2000 |
| 49 | Multicast topology inference from measured end-to-end loss - Duffield, Horowitz, et al. - 2002 |
| 49 | Network tomography: Recent developments - Castro, Coates, et al. - 2004 |
| 48 | PC Based precision timing without GPS - Pásztor, Veitch |
| 1 | Inference and labeliing of metric-induced network topologies - Bestavros, Byers, et al. - 2002 |
