Libra: A Congestion Control Framework for Diverse Application Preferences and Network Conditions

With the increase of diversity in application preferences and networks, existing congestion control algorithms (CCAs) do not accommodate this complicated reality. Previous classic CCAs are designed for a specific domain with fixed rules, failing to adapt to such diversities. Recently surged learning-based CCAs have great potential in adaptability and flexibility but are not practical due to unsatisfying performance on convergence, fairness, overhead, consistency and safety assurance. In this paper, we propose Libra, a unified congestion control framework, that can empower these properties by combining the wisdom of classic and reinforcement learning (RL)-based CCAs. Extensive evaluation of Libra’s Linux kernel implementations on both live Internet and emulated networks shows performance improvement under dynamic networks (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.2\times $ </tex-math></inline-formula> throughput than Orca on average). At the same time, Libra can flexibly satisfy different application needs, reduce the running overhead by at most <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.88\times $ </tex-math></inline-formula> and perform good fairness and convergence properties, well-fitting our theoretical analysis.