MORS: Traffic-Aware Routing based on Temporal Attributes for Model Training Clusters

To train large AI models, clusters are constructed with abundant connectivity and bandwidth; but the commodity protocol ECMP and recent proposals fail to fully utilize the network bandwidth for AI traffic pattern. As model training jobs and AI clusters exhibit a predictable and periodic traffic pattern, so in this paper, we propose a MOdel training Routing System — MORS — for traffic routing in AI clusters. MORS defines temporal attributes to characterize the periodic traffic pattern of flows and network links, and temporal quality to quantify whether a path could deliver a flow quickly in the near future. MORS runs In-band Network Telemetry (INT) to collect temporal attributes of the network, and periodic analysis to extend the collected attributes in the time domain. Based on the time series of link utilization and latency, MORS computes the temporal quality of candidate paths. It enforces high-quality path selection while maintaining compatibility with commodity ECMP by manipulating the source UDP port to ensure the flow complies with the target path in the ECMP protocol. MORS is light-weight and readily deployable in the RDMA commodity cluster. Our prototype and experiments demonstrate that MORS achieves performance comparable to adaptive routing and delivers up to 14% and 50% better FCT than PLB and ECMP, respectively.