Divide and Conquer in Loss Tomography - Top Down vs. Botton Up

Abstract

Loss tomography has received considerable attention in recent years. A number of methods, either based on maximum likelihood (ML) or Bayesian reasoning, have been proposed to estimate the loss rates of a network, and almost all of them use an iterative approximating method to search for the maximum in a multi-dimensional space. Those approaches lead to the concerns of their scalability and accuracy. To overcome the problems, a bottom up method has been proposed recently, that is a near optimal solution. In this paper, we present a closed form maximum likelihood estimate (MLE) that can be implemented in a top down method. Then, the bottom up method is compared with the top down one that shows they are little difference. More, simulations conducted under various conditions show that these two methods have almost identical results. Apart from that, the bottom up approach is independent to the number of sources used to send probes to receivers, this makes it a good candidate to estimate the loss rates of a general topology.