当BBR遇上直播
When BBR Meets Live Streaming
AI总结：
针对BBR在直播场景中带宽估计不准确导致的问题，提出辅助组件BBR-Copilot通过主动发送额外数据生成精确带宽样本，提升BBR在直播中的性能。
摘要：
最近，亚马逊、腾讯、字节跳动和华为等行业先驱已采用BBR作为其直播应用（包括TikTok Live）的拥塞控制算法。然而，BBR最初是为批量数据传输而设计的，在直播场景中面临多重挑战。在本文中，我们首先探讨了由于直播场景中带宽估计不准确而导致的BBR的两个关键问题：（i）BBR难以退出启动阶段，导致严重的自致丢包。（ii）BBR在稳定阶段以低于可用带宽的速率发送数据。然后，我们提出了BBR-Copilot，一个与BBR协作的辅助拥塞控制组件，使BBR更好地适应直播场景。BBR-Copilot通过智能地创建和发送额外数据，主动生成准确的带宽测量样本。我们在QUIC上实现了BBR-Copilot原型，并通过测试平台进行了评估。实验评估结果表明，BBR-Copilot有效提升了BBR在直播场景中的性能。
英文摘要：
Recently, industrial pioneers like Amazon, Tencent, ByteDance, and Huawei have been adopting BBR as their congestion control algorithm for live-streaming applications, including TikTok Live. However, BBR, originally crafted for bulk data transmission, faces multiple challenges in live-streaming scenarios. In this paper, we first explore two key issues associated with BBR due to inaccurate bandwidth estimation in live-streaming scenarios: (i) BBR cannot easily exit its startup phase, resulting in a fierce self-inflicted loss. (ii) BBR sends data at a lower rate than the available bandwidth during its stable phase. We then propose BBR-Copilot, an auxiliary congestion control component that cooperates with BBR, making BBR better adapt to live-streaming scenarios. BBR-Copilot allows for proactively generating accurate bandwidth measurement samples by smartly creating and sending extra data. We implement the BBR-Copilot prototype upon QUIC and evaluate it via testbed. Experimental evaluation results show that BBR-Copilot effectively enhances BBR's performance in live-streaming scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
