Stochastic Fluid Theory for P2P Streaming Systems

TLDR

The tractable model yields closed‑form expressions that illuminate the fundamental behavior of P2P streaming systems. The study develops a simple stochastic fluid model to expose the fundamental characteristics and limitations of P2P streaming systems. The model incorporates peers’ real‑time demand, churn, heterogeneous upload capacity, limited infrastructure capacity, and buffering and playback delay. The model reveals that performance hinges on a critical traffic‑load ratio, with moderate‑to‑large systems performing well when the ratio exceeds this threshold, large systems outperforming small ones due to greater resilience to churn, and buffering providing a greater performance boost than extra infrastructure bandwidth in the critical region.

Abstract

We develop a simple stochastic fluid model that seeks to expose the fundamental characteristics and limitations of P2P streaming systems. This model accounts for many of the essential features of a P2P streaming system, including the peers' realtime demand for content, peer churn (peers joining and leaving), peers with heterogeneous upload capacity, limited infrastructure capacity, and peer buffering and playback delay. The model is tractable, providing closed-form expressions which can be used to shed insight on the fundamental behavior of P2P streaming systems. The model shows that performance is largely determined by a critical value. When the system is of moderate-to-large size, if a certain ratio of traffic loads exceeds the critical value, the system performs well; otherwise, the system performs poorly. Furthermore, large systems have better performance than small systems since they are more resilient to bandwidth fluctuations caused by peer churn. Finally, buffering can dramatically improve performance in the critical region, for both small and large systems. In particular, buffering can bring more improvement than can additional infrastructure bandwidth.