@article{abrol2026BBR,
title={BBR Congestion Control Algorithms: Evolution, Challenges and Future Directions},
author={Abrol, Akshita and Murali Mohan, Purnima and Truong-Huu, Tram and Gurusamy, Mohan},
url={https://dl.acm.org/doi/full/10.1145/3793537},
urldate={2026-08-19},
DOI={10.1145/3793537},
number={9},
journal={ACM Computing Surveys},
volume={58},
ISSN={1557-7341},
publisher={Association for Computing Machinery (ACM)},
year={2026},
month={02},
day={25},
pages={1--36},
abstract={Congestion control (CC) is fundamental for reliable transport layer protocols like TCP. In next-generation networks (NGN), including 5G-Advanced (5GA)/6G, CC algorithms are even more crucial due to the diversity, heterogeneity, and complexity of emerging applications. Achieving performance guarantees while ensuring fairness among NGN applications is increasingly challenging. TCP loss-based congestion control, introduced in the 1980s with packet loss as the primary indicator of "congestion", has become less effective as the correlation between packet loss and actual congestion has weakened in next-generation networks (NGN). Google developed the Bottleneck Bandwidth and Round-trip propagation time (BBR) algorithm in 2016 as an alternative to loss-based congestion control. This survey reviews the improvement of the BBR algorithm since its first release. We provide a comprehensive algorithmic analysis of BBRv1, BBRv2, and BBRv3---focusing on performance, fairness, and literature-proposed improvements to address the drawbacks of each BBR-variant. We experimentally evaluate BBRv3 with 5GA use cases, analyzing its ability to utilize bottleneck bandwidth across diverse Quality of Service (QoS) requirements in throughput and latency. Challenges persist in balancing fairness and optimizing buffering capacity for NGN applications. Finally, with the rapid adoption of artificial intelligence (AI) in networks, we discuss BBR enhancements and future intelligent CC.},
}