BBRv2 +：通过基于延迟的带宽探测平衡主动性和公平性
BBRv2+:Towards Balancing Aggressiveness and Fairness with Delay-based Bandwidth Probing
摘要：
Google提出的BBR v2旨在解决BBR在基于丢失的拥塞控制算法（CCA）中的不公平性和浅缓冲网络中过度重传的缺点。在本文中，我们首先全面研究BBRv2的性能在各种网络条件下，BBRv2减轻了BBR的缺点。尽管如此，BBRv2的好处也伴随着一些代价，包括对带宽动态的缓慢响应以及对随机丢失的低弹性。然后，我们提出BBRv2+解决BBRv2的性能问题，而不牺牲其优势BBR。为此，BBRv2+将延迟信息纳入其路径模型，这谨慎地引导其带宽探测的积极性，以不降低其对基于丢失的CCA的公平性。BBRv2+还集成了提高对随机丢失和网络抖动的弹性的机制。大量的实验证明了BBRv2+的有效性。特别是，它实现了25%的高吞吐量和可比的排队延迟相比，BBRv2在高移动性的网络场景。
英文摘要：
BBRv2, proposed by Google, aims at addressing BBR's shortcomings of unfairness against loss-based congestion control algorithms (CCAs) and excessive retransmissions in shallow-buffered networks. In this paper, we first comprehensively study BBRv2's performance under various network conditions and show that BBRv2 mitigates the shortcomings of BBR. Nevertheless, BBRv2's benefits come with several costs, including the slow responsiveness to bandwidth dynamics as well as the low resilience to random losses. We then propose BBRv2+ to address BBRv2's performance issues without sacrificing its advantages over BBR. To this end, BBRv2+ incorporates delay information into its path model, which cautiously guides the aggressiveness of its bandwidth probing to not reduce its fairness against loss-based CCAs. BBRv2+ also integrates mechanisms for improved resilience to random losses as well as network jitters. Extensive experiments demonstrate the effectiveness of BBRv2+. Especially, it achieves 25% higher throughput and comparable queuing delay in comparison with BBRv2 in high-mobility network scenarios.
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