Quantifying Skype user satisfaction
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Understanding churn in peer-to-peer networks
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
A Measurement Study of a Large-Scale P2P IPTV System
IEEE Transactions on Multimedia
An Empirical Study of the Coolstreaming+ System
IEEE Journal on Selected Areas in Communications
Minimizing node churn in peer-to-peer streaming
Computer Communications
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
ShadowStream: performance evaluation as a capability in production internet live streaming networks
Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication
ShadowStream: performance evaluation as a capability in production internet live streaming networks
ACM SIGCOMM Computer Communication Review - Special october issue SIGCOMM '12
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In large-scale P2P live streaming systems, it is shown that peers in an unpopular channel often experience worse streaming quality than those in popular channels. In this paper, by analyzing 130 GB worth of traces from a large-scale P2P streaming system, UUSee, we observe that a large number of "unpopular" channels, those with dozens or hundreds of concurrent peers, tend to experience inferior streaming quality. We also notice a short lifespan in these channels, which further exacerbates streaming quality. To derive useful insights towards improving streaming performance, we seek to thoroughly characterize important factors that may cause peer volatility in unpopular channels. Specifically, we conduct a comprehensive statistical analysis on the impact of various factors on peer lifespan, using survival analysis techniques. We found that the initial buffering level, the variance of peer indegree, and the peer joining time all have important effects on the lifespan of peers.