When to use and when not to use BBR: An empirical analysis and evaluation study

This short paper presents a detailed empirical study of BBR's performance under different real-world and emulated testbeds across a range of network operating conditions. Our empirical results help to identify network conditions under which BBR outperforms, in terms of goodput, contemporary TCP congestion control algorithms. We find that BBR is well suited for networks with shallow buffers, despite its high retransmissions, whereas existing loss-based algorithms are better suited for deep buffers. To identify the root causes of BBR's limitations, we carefully analyze our empirical results. Our analysis reveals that, contrary to BBR's design goal, BBR often exhibits large queue sizes. Further, the regimes where BBR performs well are often the same regimes where BBR is unfair to competing flows. Finally, we demonstrate the existence of a loss rate “cliff point” beyond which BBR's goodput drops abruptly. Our empirical investigation identifies the likely culprits in each of these cases as specific design options in BBR's source code.