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.

Remote Direct Memory Access (RDMA) offers high performance through OS kernel bypass and has become a key technology in modern data centers. By exploiting memorysemantic operations, RDMA-based data structures can achieve high scalability and significantly reduce CPU utilization compared with traditional Ethernet-based systems. However, classic sorted indexes such asSkiplistsuffer from low throughput under RDMA memory semantics due to frequent and costly remote accesses. To achieve both scalability and high throughput for a lock-based concurrent Skiplist, this paper introduces a hybrid paradigm that combines one-sided and two-sided RDMA operations. This proposal is built upon a re-evaluation of core design choices in RDMA, including transport modes, caching strategies, and memory management. Building on this paradigm, we designHybridSkipList+, a distributed Skiplist system that integrates a client-side coherent cache with pull-based synchronization to reduce expensive network round trips, and a semi-continuous memory allocator to enhance RDMA access locality. We implementHybridSkipList+ on an eight-machine RDMA cluster and conduct extensive evaluations. Results show that HybridSkipList+ outperforms two baseline systems by up to 4.41× and 3.45× under typical workload conditions.