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DOE OSTI · 3030636

Predicting runtime and resource utilization of jobs on integrated cloud and HPC systems

Yildirim, Esma [Queensborough Community College of CUNY, Bayside, NY (United States)] (ORCID:0000000194853714)·Hussein, Mohab [Queensborough Community College of CUNY, Bayside, NY (United States)] (ORCID:0009000386027393)·Titov, Mikhail [Brookhaven National Laboratory (BNL), Upton, NY (United States)] (ORCID:0000000323577382)·Kilic, Ozgur Ozan [Brookhaven National Laboratory (BNL), Upton, NY (United States)] (ORCID:000000032129408X)
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Abstract

Recent advances in virtualization technologies used in cloud computing offer performance that closely approaches bare-metal levels. Combined with specialized instance types and high-speed networking services for cluster computing, cloud platforms have become a compelling option for high-performance computing (HPC). However, most current batch job schedulers in HPC systems are designed for homogeneous clusters and make decisions based on limited information about jobs and system status. Scientists typically submit computational jobs to these schedulers with a requested runtime that is often over- or under-estimated. More accurate runtime predictions can help schedulers make better decisions and reduce job turnaround times. Here, they can also support decisions about migrating jobs to the cloud to avoid long queue wait times in HPC systems.

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97 MATHEMATICS AND COMPUTINGBatch job schedulingCloud and HPC resource monitoringMeasurement and prediction of multi-processor systemsNeural networksPerformance modeling

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Yildirim, Esma [Queensborough Community College of CUNY, Bayside, NY (United States)] (ORCID:0000000194853714), Hussein, Mohab [Queensborough Community College of CUNY, Bayside, NY (United States)] (ORCID:0009000386027393), Titov, Mikhail [Brookhaven National Laboratory (BNL), Upton, NY (United States)] (ORCID:0000000323577382), Kilic, Ozgur Ozan [Brookhaven National Laboratory (BNL), Upton, NY (United States)] (ORCID:000000032129408X). 2025-11-03. Predicting runtime and resource utilization of jobs on integrated cloud and HPC systems. https://doi.org/10.1016/j.future.2025.108230

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