Markovich, Natalia M.Natalia M.MarkovichKrieger, Udo R.Udo R.Krieger2019-09-192014-01-272013978-3-642-36783-0978-3-642-36784-7https://fis.uni-bamberg.de/handle/uniba/2536Applying advanced statistical techniques, we characterize the peculiarities of a locally observed peer population in a popular P2P overlay network. The latter is derived from a mesh-pull architecture. Using flow data collected at a single peer, we show how Pareto and Generalized Pareto models can be applied to classify the local behavior of the population feeding a peer. Our approach is illustrated both by file sharing data of a P2P session generated by a mobile BitTorrent client in a WiMAX testbed and by video data streamed to a stationary client in a SopCast session. These techniques can help us to cope with an efficient adaptation of P2P dissemination protocols to mobile environments.engheavy hitter modelGeneralized Pareto distributionpeer-to-peer networkchange-point detectionbookpart10.1007/978-3-642-36784-7_5