Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
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Article Number | 04027 | |
Number of page(s) | 7 | |
Section | Distributed Computing | |
DOI | https://doi.org/10.1051/epjconf/202429504027 | |
Published online | 06 May 2024 |
https://doi.org/10.1051/epjconf/202429504027
JIRIAF: JLAB Integrated Research Infrastructure Across Facilities
JLAB, CST Division, 12000 Jefferson Avenue, Newport News, Virginia, 23606, USA
Published online: 6 May 2024
The JIRIAF project aims to combine geographically diverse computing facilities into an integrated science infrastructure. This project starts by dynamically evaluating temporarily unallocated or idled compute resources from multiple providers. These resources are integrated to handle additional workloads without affecting local running jobs. This paper describes our approach to launch best-effort batch tasks that exploit these underutilized resources. Our system measures the real-time behavior of jobs running on a machine and learns to distinguish typical performance from outliers. Unsupervised ML techniques are used to analyze hardware-level performance measures, followed by a real-time crosscorrelation analysis to determine which applications cause performance degradation. We then facilitate bad behavior by throttling these processes. We demonstrate that problematic performance interference can be detected and acted on, which makes it possible to continue to share resources between applications and simultaneously maintain high utilization levels in a computing cluster. For a case study, we relocated the CLAS12 data processing workflow to a remote data processing facility, preventing file migration and temporal data persistency.
© 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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