| Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
|---|---|---|
| Article Number | 07007 | |
| Number of page(s) | 7 | |
| Section | Facilities and Virtualization | |
| DOI | https://doi.org/10.1051/epjconf/202429507007 | |
| Published online | 06 May 2024 | |
https://doi.org/10.1051/epjconf/202429507007
Automatic Monitoring of Large-Scale Computing Infrastructure
Department of Physics, University of Florida, Gainesville, FL 32611, U.S.A.
* Corresponding author: bockjoo@phys.ufl.edu
Published online: 6 May 2024
Modern distributed computing systems produce large amounts of monitoring data. For these systems to operate smoothly, underperforming or failing components must be identified quickly, and preferably automatically, enabling the system managers to react accordingly. In this contribution, we analyze jobs and transfer data collected in the running of the LHC computing infrastructure. The monitoring data is harvested from the Elasticsearch database and converted to formats suitable for further processing. Based on various machine and deep learning techniques, we develop automatic tools for continuous monitoring of the health of the underlying systems. Our initial implementation is based on publicly available deep learning tools, PyTorch or TensorFlow packages, running on state-of-the-art GPU systems.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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