论文标题
基于模型的BBR性能，公平性和稳定性的见解
Model-Based Insights on the Performance, Fairness, and Stability of BBR
论文作者
论文摘要
Google的BBR是最近对高效，公平和灵活的拥塞控制算法（CCA）的最突出的结果。尽管许多研究已经研究了BBR的表现，但以前的工作仍然在理解BBR绩效的差距中留下了差距：基于实验的研究通常仅考虑研究人员可以通过可管理的努力来建立的网络设置，而基于模型的研究忽略了诸如融合之类的重要问题。 为了补充先前的BBR分析，本文介绍了BBRV1和BBRV2的流体模型，可以在各种网络设置和分析治疗（例如稳定性分析）下进行有效的模拟。通过实验验证，我们表明我们的流体模型提供了对BBR行为的高度准确预测。通过广泛的模拟和理论分析，我们对两个BBR版本都有了一些见解，包括BBRV2中以前未知的Bufferbloat问题。
Google's BBR is the most prominent result of the recently revived quest for efficient, fair, and flexible congestion-control algorithms (CCAs). While the performance of BBR has been investigated by numerous studies, previous work still leaves gaps in the understanding of BBR performance: Experiment-based studies generally only consider network settings that researchers can set up with manageable effort, and model-based studies neglect important issues like convergence. To complement previous BBR analyses, this paper presents a fluid model of BBRv1 and BBRv2, allowing both efficient simulation under a wide variety of network settings and analytical treatment such as stability analysis. By experimental validation, we show that our fluid model provides highly accurate predictions of BBR behavior. Through extensive simulations and theoretical analysis, we arrive at several insights into both BBR versions, including a previously unknown bufferbloat issue in BBRv2.
