Network tomography based on additive metrics

Inference of the network structure (e.g., routing topology) and dynamics (e.g., traffic matrices, link performance) is an important component in many network design and management tasks. In this paper we propose a new, general framework for designing and analyzing network inference algorithms based on additive metrics using ideas and tools from phylogenetic inference. Based on the framework we introduce and develop several polynomial-time distance-based inference algorithms. We provide sufficient conditions for the correctness of the algorithms. We show that the algorithms are consistent (the probability of returning correct topology and link performance parameters goes to 1 with increasing sample size) and achieve the optimal l_infin radius among all distance-based topology inference algorithms.