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This work presents a framework for estimating job wait times in High-Performance Computing (HPC) scheduling queues, leverag- ing historical job scheduling data and real-time system metrics. Using machine learning techniques, specifically Random Forest and Multi-Layer Perceptron (MLP) models, we demonstrate high accuracy in predicting wait times, achieving 94.2% reliability within a 10-minute error margin. The framework incorporates key fea- tures such as requested resources, queue occupancy, and system utilization, with ablation studies revealing the significance of these features. Additionally, the framework offers users wait time esti- mates for different resource configurations, enabling them to select optimal resources, reduce delays, and accelerate computational workloads. Our approach provides valuable insights for both users and administrators to optimize job scheduling, contributing to more efficient resource management and faster time to scientific results.
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This content will become publicly available on November 17, 2025
A Hierarchical Deep Learning Approach for Predicting Job Queue Times in HPC Systems
Accurate wait-time prediction for HPC jobs contributes to a positive user experience but has historically been a challenging task. Previous models lack the accuracy needed for confident predictions, and many were developed before the rise of deep learning. In this work, we investigate and develop TROUT, a neural network-based model to accurately predict wait times for jobs submitted to the Anvil HPC cluster. Data was taken from the Slurm Workload Manager on the cluster and transformed before performing additional feature engineering from jobs’ priorities, partitions, and states. We developed a hierarchical model that classifies job queue times into bins before applying regression, outperforming traditional methods. The model was then integrated into a CLI tool for queue time prediction. This study explores which queue time prediction methods are most applicable for modern HPC systems and shows that deep learning-based prediction models are viable solutions.
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- Award ID(s):
- 2005632
- PAR ID:
- 10639606
- Publisher / Repository:
- IEEE Xplore
- Date Published:
- Page Range / eLocation ID:
- 621 to 628
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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