## Hierarchical Inference of Unicast Network Topologies Based on End-to-End Measurements

Citations: | 2 - 0 self |

### BibTeX

@MISC{Shih_hierarchicalinference,

author = {Meng-fu Shih and Alfred O. Hero},

title = {Hierarchical Inference of Unicast Network Topologies Based on End-to-End Measurements},

year = {}

}

### OpenURL

### Abstract

Abstract—In this paper, we address the problem of topology discovery in unicast logical tree networks using end-to-end measurements. Without any cooperation from the internal routers, topology estimation can be formulated as hierarchical clustering of the leaf nodes based on pairwise correlations as similarity metrics. Unlike previous work that first assumes the network topology is a binary tree and then tries to generalize to a nonbinary tree, we provide a framework that directly deals with general logical tree topologies. A hierarchical algorithm to estimate the topology is developed in a recursive manner by finding the best partitions of the leaf nodes level by level. Our simulations show that the algorithm is more robust than binary-tree based methods. Index Terms—Graph-based clustering, mixture models, network tomography, topology estimation. I.

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Citation Context ...ironments. Section VI provides the conclusion and discusses future work. For more detailed derivations and more simulation studies than what could be presented in this paper the reader is referred to =-=[12]-=-. II. BACKGROUND A. Problem Formulation Our work focuses on the problem of estimating logical tree network structures given end-to-end statistics measured by probes sent from the root to the leaf node... |