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 TRO...

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Published in:SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis pp. 621 - 628
Main Authors: Lovell, Austin, Wisniewski, Philip, Rodenbeck, Sarah, Ashish
Format: Conference Proceeding
Language:English
Published: IEEE 17.11.2024
Subjects:
Accuracy
computational efficiency
Computational modeling
Conferences
Deep learning
High performance computing
machine learning
neural networks
operations research
performance optimization
Predictive models
queue management
Queueing analysis
resource allocation
Resource management
User experience
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Abstract 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.
AbstractList 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.
Author Rodenbeck, Sarah
Ashish
Lovell, Austin
Wisniewski, Philip
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  surname: Ashish
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  email: ashish@purdue.edu
  organization: Purdue University,Rosen Center for Advanced Computing,Indiana,USA
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Snippet Accurate wait-time prediction for HPC jobs contributes to a positive user experience but has historically been a challenging task. Previous models lack the...
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SubjectTerms Accuracy
computational efficiency
Computational modeling
Conferences
Deep learning
High performance computing
machine learning
neural networks
operations research
performance optimization
Predictive models
queue management
Queueing analysis
resource allocation
Resource management
User experience
Title A Hierarchical Deep Learning Approach for Predicting Job Queue Times in HPC Systems
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