Maxtream: Stabilizing P2P streaming by active prediction of behavior patterns

In theory, peer-to-peer (P2P) based streaming designs and simulations provide a promising alternative to server-based streaming systems both in cost and scalability. In practice however, implementations of P2P based IPTV and VOD failed to provide a satisfying QoS as the characteristic fluctuational throughput of a peer's uplink leads to frequent annoying hiccups, substantial delays and latency for those who download from it. A significant factor for the unstable throughput of peers' uplink is the behavior of other processes running on the source peer that consume bandwidth resources. In this paper we propose Maxtream - a machine learning based solution that actively predicts load in the uplink of streaming peers and coordinates source peers exchanges between peers that suffer from buffer underrun and peers that enjoy satisfactory buffer size for coping with future problems. Simulation and experiments have shown that the solution successfully predicts upcoming load in popular protocols and can improve the QoS in existing P2P streaming networks.