Self-diagnostic peer-assisted video streaming through a learning framework

Di Niu, Baochun Li, Shuqiao Zhao

Abstract: Quality control and resource optimization are challenging problems in peer-assisted video streaming systems, due to their large scales and unreliable peer behavior. Such systems are also prone to per- formance degradation in the event of drastic demand changes, such as flash crowds and large-scale simultaneous peer departures. In this paper, we demonstrate the deficiency of state-of-the-art video streaming systems by analyzing real-world traces from UUSee, a popular commercial P2P media streaming system based in China, during the 2008 Beijing Olympics. We show how simple machine learning techniques combined with periodic collection of statistics can be used for automated monitoring and diagnosis of peer-assisted video streaming systems. With such a framework, it is possible to es- timate performance given certain resource usage patterns, making resource utilization more efficient. It also enables the prediction of large-scale performance degradation due to irregular demand pat- terns. #e effectiveness of our proposed framework is validated with extensive trace-driven evaluations.