  BBRv2, proposed by Google, aims at addressing BBR's shortcomings of
unfairness against loss-based congestion control algorithms (CCAs) and
excessive retransmissions in shallow-buffered networks. In this paper, we first
comprehensively study BBRv2's performance under various network conditions and
show that BBRv2 mitigates the shortcomings of BBR. Nevertheless, BBRv2's
benefits come with several costs, including the slow responsiveness to
bandwidth dynamics as well as the low resilience to random losses. We then
propose BBRv2+ to address BBRv2's performance issues without sacrificing its
advantages over BBR. To this end, BBRv2+ incorporates delay information into
its path model, which cautiously guides the aggressiveness of its bandwidth
probing to not reduce its fairness against loss-based CCAs. BBRv2+ also
integrates mechanisms for improved resilience to random losses as well as
network jitters. Extensive experiments demonstrate the effectiveness of BBRv2+.
Especially, it achieves 25% higher throughput and comparable queuing delay in
comparison with BBRv2 in high-mobility network scenarios.
