Adaptive Gain-Based Quick-Measurement BBR Algorithm in High BDP Network Environments

Congestion control is the main method to solve network congestion. The Bottleneck Band-Width and Round-Trip ropagation time(BBR) congestion control algorithm, proposed by Google in 2016, can achieve lower latency while maintaining higher throughput. However, in high-bandwidth, long-delay network conditions, BBR and other improved algorithms suffer from low bandwidth utilization and slow convergence. In order to ameliorate the above problems, the QM_BBR algorithm proposed in this paper, improves the transmission performance of each phase by 1) Improving the speed of the Startup phase based on comparison, 2) Adjusting the performance gain of the ProbeBw phase based on adaptation, 3) Adding a new Quick-Measurement phase based on the state judgment, which can adaptively adjust the pacing gain according to the current network latency and the network congestion to make the network congestion end more quickly. The experimental results show that QM_BBR improves the convergence speed by up to 18%, reduces the retransmission by 77%, and increases the throughput by 8.1% compared with BBR.