{"ID":2867664,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.22701","arxiv_id":"2509.22701","title":"Enhancing Cluster Scheduling in HPC: A Continuous Transfer Learning for Real-Time Optimization","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.","short_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, mini...","url_abs":"https://arxiv.org/abs/2509.22701","url_pdf":"https://arxiv.org/pdf/2509.22701v1","authors":"[\"Leszek Sliwko\",\"Jolanta Mizera-Pietraszko\"]","published":"2025-09-22T12:27:20Z","proceeding":"cs.DC","tasks":"[\"cs.DC\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}