Issue |
EPJ Web Conf.
Volume 214, 2019
23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|
|
---|---|---|
Article Number | 07015 | |
Number of page(s) | 8 | |
Section | T7 - Clouds, virtualisation & containers | |
DOI | https://doi.org/10.1051/epjconf/201921407015 | |
Published online | 17 September 2019 |
https://doi.org/10.1051/epjconf/201921407015
Backfilling the Grid with Containerized BOINC in the ATLAS computing
1
Institute of High Energy Physics, CAS,
19B Yuquan Road,
Beijing, 100049,
China
2
Department of Physics, University of Oslo,
P.b. 1048 Blindern,
N-0316 Oslo,
Norway
1 Corresponding author: wuwj@ihep.ac.cn
Published online: 17 September 2019
Virtualization is a commonly used solution for utilizing the opportunistic computing resources in the HEP field, as it provides a unified software and OS layer that the HEP computing tasks require over the heterogeneous opportunistic computing resources. However there is always performance penalty with virtualization, especially for short jobs which are always the case for volunteer computing tasks, the overhead of virtualization reduces the CPU efficiency of the jobs, hence it leads to low CPU efficiency of the jobs. With the wide usage of containers in HEP computing, we explore the possibility of adopting the container technology into the ATLAS BOINC project, hence we implemented a Native version in BOINC, which uses the Singularity container or direct usage of the Operating System of the host machines to replace VirtualBox. In this paper, we will discuss 1) the implementation and workflow of the Native version in the ATLAS BOINC; 2) the performance measurement of the Native version comparing to the previous virtualization version. 3) the limits and shortcomings of the Native version; 4) The practice and outcome of the Native version which includes using it in backfilling the ATLAS Grid Tier2 sites and other clusters, and to utilize the idle computers from the CERN computing centre.
© The Authors, published by EDP Sciences, 2019
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.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.