RTCC: Enable End-to-end Sub-RTT Congestion Control for Next-generation Network

The advancement of next-generation networks such as 5G/6G and satellite systems has significantly increased available network bandwidth, while also exacerbating network burstiness. This surge presents a formidable challenge for congestion control (CC), a pivotal mechanism for achieving high bandwidth utilization and low latency by adjusting congestion windows or modifying sending rates. Traditional end-to-end CC algorithms fall short of optimality due to their reliance on congestion signals in acknowledgment packets, which introduce a delay of one round-trip time (RTT). In this paper, to mitigate end-to-end delayed feedback, we introduce a novel Real-Time Congestion Control (RTCC) algorithm that integrates machine learning with conventional model-based CC. RTCC employs a Multi-Layer Perceptron (MLP) to model network conditions and predict current congestion signals accurately. A tailored network model then utilizes these predictions to manage packet accumulation in the network bottleneck. Moreover, an online model-updating mechanism is proposed to adapt to diverse network environments. We integrate RTCC into QUIC and conduct comprehensive experiments in both emulated test-beds and real-world settings, including WiFi/4G/5G and cross-continent networks. The results demonstrate RTCC's efficacy, with up to a 32% increase in average throughput, a reduction in RTT by up to 21% compared to BBR V2, and the maintenance of fair bandwidth allocation.