MAST: Global Scheduling of ML Training across Geo-Distributed Datacenters at Hyperscale

In public clouds, users must manually select a data-center region to upload their ML training data and launch ML training workloads in the same region to ensure data and computation colocation. Unfortunately, isolated decisions by individual users can lead to a mismatch between workload demand and hardware supply across regions, hurting the cloud provider's hardware utilization and profitability. To address this problem in Meta's hyperscale private cloud, we provide a global-scheduling abstraction to all ML training workloads. Users simply submit their training workloads to MAST, our global scheduler, and rely on it to intelligently place both data and training workloads to different regions. We describe three design principles that enable MAST to schedule complex ML training workloads at a global scale: temporal decoupling, scope decoupling, and exhaustive search. MAST successfully balances the load across global regions. Before MAST, the most overloaded region had a GPU demand-to-supply ratio of 2.63 for high-priority workloads. With MAST, this ratio has been reduced to 0.98, effectively eliminating the overload.