Packet delay greatly influences the overall performance of network applications. It is therefore important to identify causes and location of delay performance degradation within a network. Existing techniques, largely based on end-to-end delay measurements of unicast traffic, are well suited to monitor and characterize the behavior of particular end-to-end paths. Within these approaches, however, it is not clear how to apportion the variable component of end-toend delay as queueing delay at each link along a path. Moreover, they suffer of scalability issues if a significant portion of a network is of interest.In this paper, we show how end-to-end measurements of multicast traffic can be used to infer the packet delay distribution and utilization on each link of a logical multicast tree. The idea, recently introduced in [4,5] is to exploit the inherent correlation between multicast observations to infer performance of paths between branch points in a tree spanning a multicast source and its receivers. The method does not depend on cooperation from intervening network elements; because of the bandwidth efficiency of multicast traffic, it is suitable for large scale measurements of both end-to-end and internal network dynamics. We establish desirable statistical properties of the estimator, namely consistency and asymptotic normality. We evaluate the estimator through simulation and observe that it is robust with respect to moderate violations of the underlying model.

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Abstract-The use of end-to-end multicast traffic measurements has been recently proposed as a means to infer network internal characteristics as packet link loss rate and delay. In this paper, we propose an algorithm that infers the multicast tree topology based on these end-to-end measurements. Differently from previous approaches which make only partial use of the available information, this algorithm adaptively combines different performance measures to reconstruct the topology. We establish its consistency and evaluate its accuracy through simulation. We show that in general it requires many fewer probes to correctly identify the topology than other methods.

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