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Knowing the largest rate at which data can be sent on an end-to-end path suchthat the egress rate is equal to the ingress rate with high probability can bevery practical when choosing transmission rates in video streaming or selectingpeers in peer-to-peer applications. We introduce probabilistic availablebandwidth, which is defined in terms of ingress rates and egress rates oftraffic on a path, rather than in terms of capacity and utilization of theconstituent links of the path like the standard available bandwidth metric. Inthis paper, we describe a distributed algorithm, based on a probabilisticgraphical model and Bayesian active learning, for simultaneously estimating theprobabilistic available bandwidth of multiple paths through a network. Ourprocedure exploits the fact that each packet train provides information notonly about the path it traverses, but also about any path that shares a linkwith the monitored path. Simulations and PlanetLab experiments indicate thatthis process can dramatically reduce the number of probes required to generateaccurate estimates.

Multi-path Probabilistic Available Bandwidth Estimation through Bayesian Active Learning