Abstract-Like other problems in network tomography or traffic matrix estimation, the location of congested IP links from end-to-end measurements requires solving a system of equations that relate the measurement outcomes with the variables representing the status of the IP links. In most networks, this system of equations does not have a unique solution. To overcome this critical problem, current methods use the unrealistic assumption that all IP links have the same prior probability of being congested. We find that this assumption is not needed, because these probabilities can be uniquely identified from a small set of measurements by using properties of Boolean algebra. We can then use the learnt probabilities as priors to find rapidly the congested links at any time, with an order of magnitude gain in accuracy over existing algorithms. We validate our results both by simulation and real implementation in the PlanetLab network.
We address the problem of calculating link loss rates from end-to-end measurements. Contrary to existing works that use only the average end-to-end loss rates or strict temporal correlations between probes, we exploit second-order moments of end-to-end flows. We first prove that the variances of link loss rates can be uniquely calculated from the covariances of the measured end-to-end loss rates in any realistic topology. After calculating the link variances, we remove the un-congested links with small variances from the first-order moment equations to obtain a full rank linear system of equations, from which we can calculate precisely the loss rates of the remaining congested links. This operation is possible because losses due to congestion occur in bursts and hence the loss rates of congested links have high variances. On the contrary, most links on the Internet are un-congested, and hence the averages and variances of their loss rates are virtually zero. Our proposed solution uses only regular unicast probes and thus is applicable in today's Internet. It is accurate and scalable, as shown in our simulations and experiments on PlanetLab.
Abstract-Compared to wired networks, sensor networks pose two additional challenges for monitoring functions: they support much less probing traffic, and they change their routing topologies much more frequently. We propose therefore to use only endto-end application traffic to infer performance of internal network links. End-to-end data do not provide sufficient information to calculate link loss rates exactly, but enough to identify poorly performing (lossy) links. We introduce inference techniques based on Maximum likelihood and Bayesian principles, which handle well noisy measurements and routing changes. We evaluate the performance of both inference algorithms in simulation and on real network traces. We find that these techniques achieve high detection and low false positive rates.
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