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 | 04031 | |
Number of page(s) | 8 | |
Section | Distributed Computing | |
DOI | https://doi.org/10.1051/epjconf/202429504031 | |
Published online | 06 May 2024 |
https://doi.org/10.1051/epjconf/202429504031
Managing remote cloud resources for multiple HEP VOs with cloudscheduler
High Energy Physics Research Computing Group, University of Victoria, BC, Canada
* e-mail: mebert@uvic.ca
Published online: 6 May 2024
Cloudscheduler is a system to manage resources of local and remote compute clouds and makes those resources available to HTCondor pools. It examines the resource needs of idle jobs, then starts virtual machines (VMs), sized accordingly, on allowed clouds with available resources. Using yaml files, cloudscheduler then provisions the VMs during the boot process with all necessary tools needed to register with HTCondor and run the experiment’s jobs. Although we have run cloudscheduler in its first version for ATLAS and Belle-II workloads successfully for more than 10 years, we developed cloudscheduler version 2 (CSV2), a complete overhaul and modernization of cloudscheduler. The new system is used successfully in production for Belle-II, ATLAS, DUNE, and BABAR. In addition to using cloudscheduler version 2 as a WLCG site, we also run it as a service for other WLCG sites, and the Canadian Advanced Network for Astronomical Research (CANFAR) group uses its own instance of CSV2 for their astronomy workloads. In this paper, we report on our experience in operating CSV2 for different experiment’s jobs, running on up to 10,000 cores across all experiments and clouds in North America, Australia, and Europe. We will also report on how to correctly account for the resource usage in the WLCG APEL system, how the monitoring works, as well as on the integration of different clouds and how to use resources opportunistically.
© 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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