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Network tomography receives a considerable attention in recent years that provides a viable methodology to discover network charateristics, such as the loss rate of a link, the delay distribution of a link, from end-to-end observations. In the loss rate estimation, the previous studies show that to find the maximum likelihood estimate (MLE) of a link/path, we need to solve a polynomial form with a...
Delay tomography and loss tomography are two key components of network tomography, the former is for estimating link level delay distribution of a network, while the latter for estimating link-level loss rates of a network, by end-to-end measurement. The difference between the objectives leads to two independent studies and creates two types of methods for one each. Preti et. al. attempted to connect...
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...
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...
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. Almost all implementations use an iterative approximating method, (e.g. EM algorithm) to search for the maximum in a multi-dimensional space, which crates two concerns: scalability and accuracy...
Loss tomography, as a key component of network tomography, aims to obtain the loss rate of each link in a network by end-to-end measurements. If knowing the loss model of a link, we, in fact, deal with a parametric estimate problem with incomplete data. Maximum likelihood estimates are often used in this situation to identify the unknown parameters in the loss model. Almost all methods proposed so...
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