Loss Rate Estimation in General Topologies

Abstract

Loss tomography as a key component of network tomography receives considerable attention in recent years and a number of methods based on maximum likelihood estimate (MLE) and Bayesian estimate have been proposed. However, most methods proposed so far only target a treelike network, their application in practice is limited because of this. To overcome this limitation, we in this paper propose three estimation methods for networks with a general topology. We start our description from the tree structure and provide the insight into the connection between observations and loss rates, and present a closed form MLE that is obtained by solving a set of log-likelihood equations. In addition, a top down algorithm based on the closed form MLE is developed to estimate link-level loss rates from observation. Then, the closed form MLE is extended to cover a general topology consisting of a number of intersected trees. Finally, the three approximating methods, called modified weighted average, combine probe top down (CPTD) and hybrid bottom up and top down (IIBT), are proposed to estimate the loss rates of a general network. All algorithms proposed in this paper are analyzed mathematically and evaluated through simulations which show the efficiency and accuracy of the methods.