  In next-generation networks, achieving Round-trip Time (RTT) fairness is
essential for ensuring fair bandwidth distribution among diverse flow types,
enhancing overall network utilization. The TCP congestion control algorithm --
BBR, was proposed by Google to dynamically adjust sending rates in response to
changing network conditions. While BBRv2 was implemented to overcome the
unfairness limitation of BBRv1, it still faces intra-protocol fairness
challenges in balancing the demands of high-bandwidth, long-RTT elephant flows
and more frequent short-RTT mice flows. These issues lead to throughput
imbalances and queue buildup, resulting in elephant flow dominance and mice
flow starvation. In this paper, we first investigate the limitations of
Google's BBR algorithm, specifically in the context of intra-protocol RTT
fairness in beyond 5G (B5G) networks. While existing works address this
limitation by adjusting the pacing rate, it eventually leads to low throughput.
We hence develop the FaiRTT algorithm to resolve the problem by dynamically
estimating the Bandwidth Delay Product (BDP) sending rate based on RTT
measurements, focusing on equitable bandwidth allocation. By modeling the Inf
light dependency on the BDP, bottleneck bandwidth, and packet departure time
after every ACK, we can resolve the intra-protocol fairness while not
compromising the throughput on the bottleneck link. Through extensive
simulations on NS-3 and comprehensive performance evaluations, FaiRTT is shown
to significantly improve the fairness index and network throughput,
significantly outperforming BBRv2, for diverse flow types. FaiRTT achieves an
average throughput ratio of 1.08 between elephant and mice flows, an average
fairness index of 0.98, and an average utilization of the bottleneck link of
98.78%.
