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BBRv2, proposed by Google, aims at addressing BBR's shortcomings ofunfairness against loss-based congestion control algorithms (CCAs) andexcessive retransmissions in shallow-buffered networks. In this paper, we firstcomprehensively study BBRv2's performance under various network conditions andshow that BBRv2 mitigates the shortcomings of BBR. Nevertheless, BBRv2'sbenefits come with several costs, including the slow responsiveness tobandwidth dynamics as well as the low resilience to random losses. We thenpropose BBRv2+ to address BBRv2's performance issues without sacrificing itsadvantages over BBR. To this end, BBRv2+ incorporates delay information intoits path model, which cautiously guides the aggressiveness of its bandwidthprobing to not reduce its fairness against loss-based CCAs. BBRv2+ alsointegrates mechanisms for improved resilience to random losses as well asnetwork jitters. Extensive experiments demonstrate the effectiveness of BBRv2+.Especially, it achieves 25% higher throughput and comparable queuing delay incomparison with BBRv2 in high-mobility network scenarios.

BBRv2+:Towards Balancing Aggressiveness and Fairness with Delay-based Bandwidth Probing