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Surrogate Modeling for Scalable Evaluation of Distributed Computing Systems for HEP Applications.

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Titel: Surrogate Modeling for Scalable Evaluation of Distributed Computing Systems for HEP Applications.
Autoren: Schmid, Larissa, Horzela, Maximilian, Zhyla, Valerii, Giffels, Manuel, Quast, Günter, Koziolek, Anne
Quelle: EPJ Web of Conferences; 10/7/2025, Vol. 337, p1-8, 8p
Abstract: The Worldwide LHC Computing Grid (WLCG) provides the robust computing infrastructure essential for the LHC experiments by integrating global computing resources into a cohesive entity. Simulations of different compute models present a feasible approach for evaluating future adaptations that are able to cope with future increased demands. However, running these simulations incurs a trade-off between accuracy and scalability. For example, while the simulator DCSim can provide accurate results, it falls short on scaling with the size of the simulated platform. Using Generative Machine Learning as a surrogate presents a candidate for overcoming this challenge. In this work, we evaluate the usage of three different Machine Learning models for the simulation of distributed computing systems and assess their ability to generalize to unseen situations. We show that those models can predict central observables derived from execution traces of compute jobs with approximate accuracy but with orders of magnitude faster execution times. Furthermore, we identify potentials for improving the predictions towards better accuracy and generalizability. [ABSTRACT FROM AUTHOR]
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  Data: Surrogate Modeling for Scalable Evaluation of Distributed Computing Systems for HEP Applications.
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  Data: EPJ Web of Conferences; 10/7/2025, Vol. 337, p1-8, 8p
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  Data: The Worldwide LHC Computing Grid (WLCG) provides the robust computing infrastructure essential for the LHC experiments by integrating global computing resources into a cohesive entity. Simulations of different compute models present a feasible approach for evaluating future adaptations that are able to cope with future increased demands. However, running these simulations incurs a trade-off between accuracy and scalability. For example, while the simulator DCSim can provide accurate results, it falls short on scaling with the size of the simulated platform. Using Generative Machine Learning as a surrogate presents a candidate for overcoming this challenge. In this work, we evaluate the usage of three different Machine Learning models for the simulation of distributed computing systems and assess their ability to generalize to unseen situations. We show that those models can predict central observables derived from execution traces of compute jobs with approximate accuracy but with orders of magnitude faster execution times. Furthermore, we identify potentials for improving the predictions towards better accuracy and generalizability. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of EPJ Web of Conferences is the property of EDP Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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