基于损失的拥塞控制的BBR交互建模

Ranysha Ware, Matthew K. Mukerjee, S. Seshan, Justine Sherry
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引用次数: 52

摘要

BBR是针对Chromium QUIC和Linux内核部署的一种新的拥塞控制算法(CCA)。作为YouTube的默认CCA(占互联网流量的11%以上),BBR已迅速成为互联网拥塞控制的主要参与者。BBR对其他连接的公平或友好性最近受到了审查,因为多个研究小组的测量结果表明,BBR与传统cca竞争会产生不良后果。其中一个结果是,当与多达16种基于损失的算法(如Cubic或Reno)竞争时,单个BBR流消耗的链路容量比例固定为40%。在这篇短文中,我们提供了第一个捕获BBR在与基于损失的cca竞争中的行为的模型。我们的模型与实际实验相结合,以验证其含义。关键的教训是:在竞争下,BBR受到其“飞行上限”的窗口限制,这决定了BBR的带宽消耗。通过建模BBR在不同网络条件下的飞行上限值,我们可以预测BBR在与Cubic流竞争时的吞吐量,中位数误差为5%,与Reno流竞争时的中位数误差为8%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling BBR's Interactions with Loss-Based Congestion Control
BBR is a new congestion control algorithm (CCA) deployed for Chromium QUIC and the Linux kernel. As the default CCA for YouTube (which commands 11+% of Internet traffic), BBR has rapidly become a major player in Internet congestion control. BBR's fairness or friendliness to other connections has recently come under scrutiny as measurements from multiple research groups have shown undesirable outcomes when BBR competes with traditional CCAs. One such outcome is a fixed, 40% proportion of link capacity consumed by a single BBR flow when competing with as many as 16 loss-based algorithms like Cubic or Reno. In this short paper, we provide the first model capturing BBR's behavior in competition with loss-based CCAs. Our model is coupled with practical experiments to validate its implications. The key lesson is this: under competition, BBR becomes window-limited by its 'in-flight cap' which then determines BBR's bandwidth consumption. By modeling the value of BBR's in-flight cap under varying network conditions, we can predict BBR's throughput when competing against Cubic flows with a median error of 5%, and against Reno with a median of 8%.
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