Emerging large scale Internet applications such as IPTV, VOD and File Sharing base their infrastructure on P2P technology. Yet, the characteristic fluctuational throughput of source peers affect the QOS of such applications which might be reflected by a reduced download rate in file sharing or even worse - annoying freezes in a streaming service. A significant factor for the unstable supply of source peers is the behavior of other processes running on the source peer that consume bandwidth resources. In this paper we present Collabrium - a collaborative solution that employs a machine learning approach to actively predict load in the uplink of source peers and alert their clients to replace their source. Experiments on home machines demonstrated successful predictions of upcoming loads and Collabrium learned the behavior of popular heavy bandwidth consuming protocols such as eMule & BitTorrent correctly with no prior knowledge.
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