用强化学习动态调度异构GPU集群上的深度学习任务，提升效率与公平性。
Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters
- 结合强化学习优先级与整数规划映射，动态优化任务调度。
- 在真实数据集上实现高达20%的显卡利用率、70%的完成时间缩短。
- 无需针对每项任务调优，适合大规模云平台部署使用。
现代云平台日益承载大规模深度学习工作负载，要求高吞吐、低延迟的GPU调度。然而，GPU集群异构性增强及应用特征可见性不足，使现有调度器面临挑战——它们通常依赖离线分析或特定应用假设。本文提出RLTune，一种无需应用先验知识的强化学习调度框架，可动态分配异构GPU集群上的深度学习任务。该框架融合强化学习驱动的任务优先级排序与混合整数规划（MILP）的任务-节点映射，以优化整体系统目标，如作业完成时间（JCT）、排队延迟和资源利用率。基于微软Philly、Helios和阿里云的大规模生产日志训练，RLTune将显卡利用率提升最高达20%，排队延迟降低最高81%，作业完成时间缩短最多70%。相比以往方法，RLTune无需每任务单独调优，具备跨多样化工作负载的泛化能力，适用于云服务商规模化部署，实现更高效、公平、可持续的深度学习工作负载管理。
原文摘要 · Abstract (English)
Modern cloud platforms increasingly host large-scale deep learning (DL) workloads, demanding high-throughput, low-latency GPU scheduling. However, the growing heterogeneity of GPU clusters and limited visibility into application characteristics pose major challenges for existing schedulers, which often rely on offline profiling or application-specific assumptions. We present RLTune, an application-agnostic reinforcement learning (RL)-based scheduling framework that dynamically prioritizes and allocates DL jobs on heterogeneous GPU clusters. RLTune integrates RL-driven prioritization with MILP-based job-to-node mapping to optimize system-wide objectives such as job completion time (JCT), queueing delay, and resource utilization. Trained on large-scale production traces from Microsoft Philly, Helios, and Alibaba, RLTune improves GPU utilization by up to 20%, reduces queueing delay by up to 81%, and shortens JCT by as much as 70 percent. Unlike prior approaches, RLTune generalizes across diverse workloads without requiring per-job profiling, making it practical for cloud providers to deploy at scale for more efficient, fair, and sustainable DL workload management.
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