End-to-end inference of link level queueing delay statistics

Characterizing delay distribution over the links of a network provides a remarkable amount of information which can be useful for troubleshooting, traffic engineering, adaptive multimedia flow coding, overlay network design, etc. Since querying each and every node of a path in order to retrieve this kind of information can be unfeasible or just too resource demanding, the recent research trend is to infer the internal state of a network by means of end to end measurements. Many algorithms in literature require active measurements and are based on a single-sender multiple-receivers scheme, thus relying on the cooperation of a possibly wide number of nodes, which is a quite strong assumption. Moreover, many previous works adopt Expectation-Maximization algorithms to cope with large and under-determined equation systems, thus increasing the uncertainty of the final delay estimation. This paper, instead, proposes a technique to infer the cumulants of the delay distribution over each link of a given network path, based on two points measurements only. The cumulants, in turn, can be used to approximate the distribution function through the Edgeworth series. The results of our approach are assessed through a wide series of model-based and ns2 based simulations and show fairly good performance under different network load conditions.