  Generalized network tomography (GNT) deals with estimation of link
performance parameters for networks with arbitrary topologies using only
end-to-end path measurements of pure unicast probe packets. In this paper, by
taking advantage of the properties of generalized hyperexponential
distributions and polynomial systems, a novel algorithm to infer the complete
link metric distributions under the framework of GNT is developed. The
significant advantages of this algorithm are that it does not require: i) the
path measurements to be synchronous and ii) any prior knowledge of the link
metric distributions. Moreover, if the path-link matrix of the network has the
property that every pair of its columns are linearly independent, then it is
shown that the algorithm can uniquely identify the link metric distributions up
to any desired accuracy. Matlab based simulations have been included to
illustrate the potential of the proposed scheme.
