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Applications such as traffic engineering and network provisioning can greatly benefit from knowing, in real time, what is the largest input rate at which it is possible to transmit on a given path without causing congestion. The authors consider a probabilistic formulation for available bandwidth where the user specifies the probability of achieving an output rate almost as large as the input rate. They are interested in estimating and tracking the network-wide Probabilistic Available Bandwidth (PAB) on multiple paths simultaneously with minimal overhead on the network. They propose a novel framework based on chirps, Bayesian inference, belief propagation and active sampling to estimate the PAB.
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