Kairos: Deterministic Scheduling Enhanced by User Collaboration for Deep Learning Workloads

作者：Xinhua Wang, Weiwei Lin, Ruichao Mo, Guozhi Liu, Haijie Wu, Shengjun Tang · 发表于：IEEE Transactions on Parallel and Distributed Systems · 年份：2025 · DOI：10.1109/tpds.2025.3624245 · 被引用次数：1 · 研究领域：Parallel Computing and Optimization Techniques

As deep learning (DL) workloads scale in complexity and volume, ensuring predictable job queuing times has become a critical challenge for data centers. Existing scheduling solutions primarily focus on minimizing tardiness or job completion times (JCT), often neglecting the need for deterministic queuing, particularly in dynamic and preemptive environments. This paper introducesKairos, a preemption-based scheduling framework enhanced by user collaboration to address these gaps.Kairoscombines adivide-and-conquerstrategy—segmenting jobs into sequential units with adaptive priorities—and a user-collaborative mechanism for better duration estimation. By leveraging real-time feedback from resource contention and queuing delays,Kairosminimizes a novel metric, theQueue inStability Index(QSI), achieving significant improvements in queuing predictability while maintaining competitive JCT. Experimental results demonstrate thatKairosreduces QSI by over 99.8% compared to state-of-the-art deadline-aware baselines, offering robust performance for diverse DL workloads.