Enhancing Cluster Scheduling in HPC: A Continuous Transfer Learning for Real-Time Optimization

• URL: http://arxiv.org/abs/2509.22701v1
• Date: Mon, 22 Sep 2025 12:27:20 GMT
• Title: Enhancing Cluster Scheduling in HPC: A Continuous Transfer Learning for Real-Time Optimization
• Authors: Leszek Sliwko, Jolanta Mizera-Pietraszko,
• Abstract summary: This study presents a machine learning-assisted approach to optimize task scheduling in cluster systems, focusing on node-affinity constraints.<n>The proposed continuous transfer learning model evolves dynamically during operations, minimizing retraining needs.<n> Evaluated on Google Cluster Data, the model achieves over 99% accuracy, reducing computational overhead and improving scheduling latency for constrained tasks.
• Score: 0.42970700836450487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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