GlideinBenchmark: Collecting resource information to optimize provisioning

Choosing the right resource can speed up job completion, better utilize the available hardware, and visibly reduce costs, especially when renting computers in the cloud. This was demonstrated in earlier studies on HEP- Cloud. However, the benchmarking of the resources proved to be a laborious and time-consuming process. This paper presents GlideinBenchmark, a new Web application leveraging the pilot infrastructure of GlideinWMS to benchmark resources, and it shows how to use the data collected and published by GlideinBenchmark to automate the optimal selection of resources. An experiment can select the benchmark or the set of benchmarks that most closely evaluate the performance of its workflows. GlideinBenchmark, with the help of the GlideinWMS Factory, controls the benchmark execution. Finally, a scheduler like HEPCloud’s Decision Engine can use the results to optimize resource provisioning.

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Publication Details

Journal
EPJ Web of Conferences
Published
2025-01-01
DOI
https://doi.org/10.1051/epjconf/202533701302
Primary Topic
Transportation and Mobility Innovations
Type
article
Field-Weighted Citation Impact
0.00

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article

GlideinBenchmark: Collecting resource information to optimize provisioning

Shrijan Swaminathan, Marco Mambelli
EPJ Web of Conferences
Transportation and Mobility Innovations
article

GlideinBenchmark: Collecting resource information to optimize provisioning

Shrijan Swaminathan, Marco Mambelli
article en

Abstract

Choosing the right resource can speed up job completion, better utilize the available hardware, and visibly reduce costs, especially when renting computers in the cloud. This was demonstrated in earlier studies on HEP- Cloud. However, the benchmarking of the resources proved to be a laborious and time-consuming process. This paper presents GlideinBenchmark, a new Web application leveraging the pilot infrastructure of GlideinWMS to benchmark resources, and it shows how to use the data collected and published by GlideinBenchmark to automate the optimal selection of resources. An experiment can select the benchmark or the set of benchmarks that most closely evaluate the performance of its workflows. GlideinBenchmark, with the help of the GlideinWMS Factory, controls the benchmark execution. Finally, a scheduler like HEPCloud’s Decision Engine can use the results to optimize resource provisioning.

EPJ Web of ConferencesVol. 337
Fermi National Accelerator Laboratory (US), Purdue University West Lafayette (US)
U.S. Department of Energy, China Scholarship Council, Office of Science, Fermilab
Openalex Percentile: Top 70%
Transportation and Mobility Innovations
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GlideinBenchmark: Collecting resource information to optimize provisioning — Shrijan Swaminathan, Marco Mambelli · EPJ Web of Conferences (2025) | TGRS Research Map | TGRS