Abstract

BBRv3, the latest iteration of Google’s BBR congestion control algorithm, has shown significant performance improvements in high-bandwidth networks. However, our analysis reveals that BBRv3’s startup phase can lead to persistent fairness issues, where flows that initially acquire a larger bandwidth share maintain their advantage throughout the connection lifetime. To address this, we propose three enhanced BBRv3 variants that modify the startup behavior. Our evaluation on the FABRIC testbed demonstrates that these variants significantly improve fairness metrics, with BBRv3e1 achieving up to 15% improvement in Jain’s fairness index while preserving over 95% link utilization across diverse network scenarios. These improvements enable more equitable resource allocation in high-bandwidth networks, ensuring that BBRv3 can be deployed with confidence.

Date

2026

Authors

Imtiaz Mahmud, Kesheng Wu, Alex Sim, Anirban Mandal, Ewa Deelman

Conference

2026 International Conference on Computing, Networking and Communications (ICNC)

Pages

716-722

Publisher

IEEE