Bridging the GPU Utilization Gap: Predictive Multi-Dimensional Resource Scheduling for AI Workloads

Modern AI data centers face a critical paradox: while machine learning workloads dominate infrastructure demands, actual GPU utilization remains consistently low. Existing schedulers fail to coordinate heterogeneous resources effectively, lack predictive capabilities for dynamic workloads, and cannot balance isolation requirements with sharing optimization in multi-tenant clusters. This paper presents Wind, a novel resource scheduler that bridges the GPU utilization gap through predictive scheduling and geometric resource coordination. Wind introduces three key innovations: (1) a resource prediction framework that leverages historical execution patterns to forecast task requirements and completion times with high accuracy;(2) a unified scheduling architecture supporting isolation, sharing, preemption, and prioritization policies that eliminate resource fragmentation while maintaining performance guarantees; and (3) a Hilbert curve-based multi-dimensional scheduling algorithm that maps CPU-memory-GPU resource space to preserve spatial locality while achieving linear computational complexity.