Predicting Network Buffer Capacity for BBR Fairness
Predicting Network Buffer Capacity for BBR Fairness
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发表时间：
2022
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通讯作者：
I. Akgun;Santiago Vargas;Michael Arkhangelskiy;Andrew Burford;Michael McNeill;A. Balasubramanian;Anshul Gandhi;E. Zadok
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作者：
I. Akgun;Santiago Vargas;Michael Arkhangelskiy;Andrew Burford;Michael McNeill;A. Balasubramanian;Anshul Gandhi;E. Zadok
BBR是一种较新的TCP拥塞控制算法，具有较好的特性，但与现有的基于损失的拥塞控制算法相比，它往往不公平。这是因为BBR的发送速率是由静态参数决定的，不能很好地适应动态和多样化的网络条件。在这项工作中，我们介绍了BBR- ml，一种基于内核ml的BBR调优系统，旨在提高与基于损失的拥塞控制竞争时的公平性。为了构建BBR-ML，我们离散了网络条件搜索空间，并在2500种不同的网络条件下训练了一个模型。然后，我们修改BBR以运行内核内模型来预测网络缓冲区大小，然后将此预测用于最优参数设置。我们的初步评估结果表明，在某些情况下，BBR-ML在与Cubic竞争时可以提高高达30%的公平性。
BBR is a newer TCP congestion control algorithm with promising features, but it can often be unfair to existing loss-based congestion-control algorithms. This is because BBR’s sending rate is dictated by static parameters that do not adapt well to dynamic and diverse network conditions. In this work, we introduce BBR-ML, an in-kernel ML-based tuning system for BBR, designed to improve fairness when in competition with loss-based congestion control. To build BBR-ML, we discretized the network condition search space and trained a model on 2,500 different network conditions. We then modified BBR to run an in-kernel model to predict network buffer sizes, and then use this prediction for optimal parameter settings. Our preliminary evaluation results show that BBR-ML can improve fairness when in competition with Cubic by up to 30% in some cases.