Impact of Segment Size and Parallel Streams on TCP BBR
Impact of Segment Size and Parallel Streams on TCP BBR
复制标题
段大小和并行流对 TCP BBR 的影响
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
N. Ghani
中科院分区：
文献类型：
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作者：
J. Crichigno;Zoltan Csibi;E. Bou;N. Ghani
TCP BBR 最近被提出作为一种拥塞控制算法。 BBR 代表了过去 30 年使用的基于窗口、基于丢失的拥塞控制的颠覆。虽然 BBR 已经针对普通应用程序（例如浏览、YouTube）进行了测试，但其在移动大数据方面的用途尚未得到广泛研究。对大流传输效率影响最大的功能是并行流的使用和最大段大小 (MSS)。本文研究了在存在数据包丢失和延迟的情况下这两个功能对大流量的影响。经验结果表明，BBR 对大型 MSS 的反应比基于窗口的基于损失的算法（Cubic、Reno、HTCP）更好。同样，随着数据传输中使用的并行流数量的增加，BBR 与 Cubic、Reno 和 HTCP 之间的性能差距也会增加，有利于 BBR。例如，在具有高损坏率 (0.01%) 的 20 毫秒 RTT、10 Gbps 网络中，BBR 使用多个流的平均改进因子几乎为 4。相比之下，HTCP、Cubic 和 Reno 的改进因子低于 2。使用大型 MSS 和并行流允许 BBR 维持高吞吐量，即使存在显着的损坏率。
TCP BBR has been recently proposed as a congestion control algorithm. BBR represents a disruption from the window-based loss-based congestion control used during the last 30 years. While BBR has been tested for trivial applications (e.g., browsing, YouTube), its use for moving big data has not been extensively studied yet. Features that largely impact the efficiency of transporting big flows are the use of parallel streams and the maximum segment size (MSS). This paper studies the impact of these two features on big flows, in the presence of packet losses and latency. Empirical results demonstrate that BBR reacts better than window-based loss-based algorithms (Cubic, Reno, HTCP) to large MSS. Similarly, as the number of parallel streams used in a data transfer increases, the performance gap between BBR and Cubic, Reno, and HTCP increases in favor of BBR. For example, in a 20-millisecond RTT, 10 Gbps network with high corruption rate (0.01%), BBR's average improvement factor from using multiple streams is almost 4. In contrast, HTCP's, Cubic's, and Reno's improvement factors are below 2. Using large MSS and parallel streams permit BBR to sustain high throughput, even in the presence of a significant corruption rate.