Abstract
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.
Abstract (translated)
这项研究提出了一种利用机器学习辅助优化集群系统任务调度的方法，重点在于节点亲和性约束。传统的调度器如Kubernetes在实时适应性方面存在困难，而所提出的连续迁移学习模型则可以在运行过程中动态演变，从而减少重新训练的需求。通过谷歌集群数据的评估，该模型实现了超过99%的准确率，减少了计算开销，并提高了受限制任务的调度延迟。这一可扩展的解决方案使实时优化成为可能，推进了机器学习在集群管理中的集成，并为未来的自适应调度策略铺平了道路。
URL
https://arxiv.org/abs/2509.22701
