SSRL: A Multipath Scheduler Switching Framework on Dynamic Environment

In modern network environments, the Multipath QUIC (MPQUIC) protocol significantly enhances data transmission reliability and efficiency by leveraging multiple paths. However, the challenge lies in developing an effective scheduling algorithm that can adapt to dynamic network conditions. Existing heuristic scheduling algorithms are tailored to specific environments, while learning-based algorithms lack the capability for fine-grained scheduling. To address this, we propose SSRL (RL-based Scheduler Switcher), a framework that dynamically switches among heuristic scheduling algorithms based on real-time network condition recognition. SSRL combines the advantages of both heuristic and learning-based algorithms, thereby enhancing MPQUIC's performance by reducing latency and improving bandwidth utilization while consuming less reorder buffer. We also design a scheduler selection model that leverages LSTM and Double DQN, enabling SSRL to understand network conditions better and make more effective scheduling decisions. We implement SSRL using Pytorch and conduct extensive evaluations with the NS3 network emulator. The results show that SSRL increases throughput by 23% and reduces RTT by 10%.