Gsched: Coordinated Flow-Control and Priority Scheduling for DNN Training in AI Cluster

Distributed deep neural network (DNN) training in multi-tenant cloud environments generates long-lived, bandwidth-intensive, and interdependent flows that challenge traditional fairness-based network scheduling. We present Gsched, a framework that orchestrates centralized planning with hardware priority enforcement to realize communication interleaving, thereby eliminating contention and maximizing pipeline efficiency. Unlike methods relying on complex rate control or custom switch hardware, Gsched leverages standard commodity switch features like DSCP and SPQ to enforce strict priority scheduling. It employs a cold-start profiling mechanism to capture task characteristics without user intervention, and a centralized solver to compute the optimal interleaving order that maximizes Average Transmission Efficiency (ATE). Our analysis proves that this prioritization strategy deterministically drives the system to a contention-free state, while a local perturbation theorem guarantees robustness against distributed execution conflicts via network backpressure. Across workloads, Gsched achieves performance close to the state-of-the-art Crux under heterogeneous workloads and remains competitive in highly diverse cases. More importantly, Gsched provides a theoretical foundation that justifies scalable per-bottleneck scheduling in multi-bottleneck networks.