Google published the first release of the Bottleneck Bandwidth and Round-trip Time (BBR) congestion control algorithm in 2016. Since then, BBR has gained a widespread attention due to its ability to operate efficiently in the presence of packet loss and in scenarios where routers are equipped with small buffers. These characteristics were not attainable with traditional loss-based congestion control algorithms such as CUBIC and Reno. BBRv2 is a recent congestion control algorithm proposed as an improvement to its predecessor, BBRv1. Preliminary work suggests that BBRv2 maintains the high throughput and the bounded queueing delay properties of BBRv1. However, the literature has been missing an evaluation of BBRv2 under different network conditions.

This paper presents an experimental evaluation of BBRv2 Alpha (v2alpha-2019-07-28) on Mininet, considering alternative active queue management (AQM) algorithms, routers with different buffer sizes, variable packet loss rates and round-trip times (RTTs), and small and large numbers of TCP flows. Emulation results show that BBRv2 tolerates much higher random packet loss rates than loss-based algorithms but slightly lower than BBRv1. The results also confirm that BBRv2 has better coexistence with loss-based algorithms and lower retransmission rates than BBRv1, and that it produces low queuing delay even with large buffers. When a Tail Drop policy is used with large buffers, an unfair bandwidth allocation is observed among BBRv2 and CUBIC flows. Such unfairness can be reduced by using advanced AQM schemes such as FQ-CoDel and CAKE. Regarding fairness among BBRv2 flows, results show that using small buffers produces better fairness, without compromising high throughput and link utilization. This observation applies to BBRv1 flows as well, which suggests that rate-based model-based algorithms work better with small buffers. BBRv2 also enhances the coexistence of flows with different RTTs, mitigating the RTT unfairness problem noted in BBRv1. Lastly, the paper presents the advantages of using TCP pacing with a loss-based algorithm, when the rate is manually configured a priori. Future algorithms could set the pacing rate using explicit feedback generated by modern programmable switches.

Google于2016年发布了第一个版本的瓶颈带宽和往返时间（BBR）拥塞控制算法。此后，由于BBR能够在丢包的情况下以及在存在路由器的情况下有效运行，因此受到了广泛的关注。配有小型缓冲器。使用传统的基于损耗的拥塞控制算法（例如CUBIC和Reno）无法获得这些特性。BBRv2是最近提出的一种拥塞控制算法，是对其前身BBRv1的改进。初步工作表明，BBRv2保持了BBRv1的高吞吐量和有限的排队延迟特性。但是，文献一直缺少在不同网络条件下对BBRv2的评估。

本文介绍了Mininet上的BBRv2 Alpha（v2alpha-2019-07-28）的实验评估，其中考虑了备用主动队列管理（AQM）算法，具有不同缓冲区大小，可变丢包率和往返时间（RTT）的路由器，以及越来越多的TCP流。仿真结果表明，与基于丢失的算法相比，BBRv2可以忍受更高的随机数据包丢失率，但比BBRv1稍低。结果还证实，与基于损耗的算法相比，BBRv2具有更好的共存性，并且比BBRv1具有更低的重传速率，并且即使具有较大的缓冲区，它也可以产生较低的排队延迟。当尾部丢弃策略与大缓冲区一起使用时，会在BBRv2和CUBIC流之间观察到不公平的带宽分配。通过使用高级AQM方案（例如FQ-CoDel和CAKE）可以减少这种不公平。关于BBRv2流之间的公平性，结果表明，使用较小的缓冲区可产生更好的公平性，而不会影响高吞吐量和链路利用率。此观察结果也适用于BBRv1流，这表明基于速率的基于模型的算法在较小的缓冲区中效果更好。BBRv2还增强了具有不同RTT的流的共存，从而减轻了BBRv1中提到的RTT不公平问题。最后，本文介绍了在先验地手动配置速率时，使用基于损耗的算法的TCP起搏的优势。未来的算法可以使用现代可编程开关生成的显式反馈来设置起搏速率。这表明基于速率的基于模型的算法在较小的缓冲区中效果更好。BBRv2还增强了具有不同RTT的流的共存，从而减轻了BBRv1中提到的RTT不公平问题。最后，本文介绍了在先验地手动配置速率时，使用基于损耗的算法的TCP起搏的优势。未来的算法可以使用现代可编程开关生成的显式反馈来设置起搏速率。这表明基于速率的基于模型的算法在较小的缓冲区中效果更好。BBRv2还增强了具有不同RTT的流的共存，从而减轻了BBRv1中提到的RTT不公平问题。最后，本文介绍了在先验地手动配置速率时，使用基于损耗的算法的TCP起搏的优势。未来的算法可以使用现代可编程开关生成的显式反馈来设置起搏速率。