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.