Modeling user behavior in P2P live video streaming systems through a Bayesian network
AIMS'10 Proceedings of the Mechanisms for autonomous management of networks and services, and 4th international conference on Autonomous infrastructure, management and security
A Bayesian approach for user aware peer-to-peer video streaming systems
Image Communication
An autonomous topology management framework for QoS enabled P2P video streaming systems
Proceedings of the 8th International Conference on Network and Service Management
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Emerging large scale Internet applications such as IPTV, VOD and File Sharing base their infrastructure on P2P technology. Yet, the characteristic fluctational 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.