Today's networks are struggling to scale and satisfy the high number and high variety of co-existing network requirements. While existing congestion control (CC) protocols are designed to handle strict classification of network flows into one or few priorities, a more granular and dynamic congestion control is needed. In this paper we present Hercules, a novel CC protocol based on an online learning approach, which supports unbounded and continues requirements space. We implemented Hercules as a QUIC module and we show, through analytical analysis and real-world experiments, that it provides between $50\%-250\%$ higher QoS for co-existing diverse network flows and outperforms state-of-the-art CC protocols, even under high network congestion.

翻译：当今网络正面临扩展性挑战，难以满足大量共存网络流需求的高度多样性与异构性。现有拥塞控制（CC）协议专为将网络流严格划分为一种或少数几种优先级而设计，因此需要更细粒度、动态的拥塞控制机制。本文提出Hercules——一种基于在线学习的新型CC协议，支持无界、连续的需求空间。我们以QUIC模块形式实现了Hercules，并通过理论分析与真实世界实验证明，即使在网络高度拥塞状态下，该协议也能为共存的多样化网络流提供50%-250%的服务质量提升，性能优于现有最先进的CC协议。