用持续迁移学习提升集群调度实时性，降低延迟。
Enhancing Cluster Scheduling in HPC: A Continuous Transfer Learning for Real-Time Optimization
- 基于持续迁移学习动态优化任务调度
- 在谷歌集群数据上达99%准确率，降低计算开销
- 适合需要实时调度的高性能计算场景
本研究提出一种机器学习辅助的集群任务调度优化方法，重点关注节点亲和性约束。传统调度器如Kubernetes在实时适应性方面表现不足，而所提出的持续迁移学习模型可在运行过程中动态演化，显著减少重新训练需求。在Google Cluster Data上的评估显示，该模型准确率超过99%，有效降低计算开销并改善受限任务的调度延迟。此可扩展方案支持实时优化，推动机器学习在集群管理中的集成，为未来自适应调度策略铺平道路。
原文摘要 · Abstract (English)
This study presents a machine learning-assisted approach to optimize task scheduling in cluster systems, focusing on node-affinity constraints. Traditional schedulers like Kubernetes struggle with real-time adaptability, whereas the proposed continuous transfer learning model evolves dynamically during operations, minimizing retraining needs. Evaluated on Google Cluster Data, the model achieves over 99% accuracy, reducing computational overhead and improving scheduling latency for constrained tasks. This scalable solution enables real-time optimization, advancing machine learning integration in cluster management and paving the way for future adaptive scheduling strategies.
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