Issue |
EPJ Web Conf.
Volume 214, 2019
23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|
|
---|---|---|
Article Number | 07028 | |
Number of page(s) | 5 | |
Section | T7 - Clouds, virtualisation & containers | |
DOI | https://doi.org/10.1051/epjconf/201921407028 | |
Published online | 17 September 2019 |
https://doi.org/10.1051/epjconf/201921407028
Experience in using public Cloud for Belle II experiment within HNSciCloud Project
1
INFN – Napoli Unit – Via Cintia.
80126,
Napoli Italy
2
GARR – Via dei Tizii, 6 00185,
Roma Italy
* Corresponding author: spardi@na.infn.it
Published online: 17 September 2019
The current level of flexibility reached by Cloud serviceproviders enables Physicists to take advantage of extra resources to extend the distributed computing infrastructure supporting High Energy Physics Experiments. However, the discussion about the optimal use of such kind of resources is currently ongoing. Moreover, because each Cloud provider offers its interfaces, API sets, and different service levels, the integration in the computing model of an experiment requires a specific work for each provider. In this paper, we summarize the experience matured in the usage of Cloud res urces offered by different providers within the Europeanproject Helix Nebula Science Cloud for Belle II experiment. The goal of the user-case was to run Montecarlo Production jobs properly on Virtual Machines created dynamically. To do that, we defined the golden image, based on CENTOS; we then set up the basic environment, the internal and external network, and local DNS services. For each cloud, we implemented a cache server based on Squid, while the orchestrator has been entralized on a single server located in the INFN-Napoli infrastructure. Finally, we integrated the available resources in the Belle II production framework based on DIRAC, using VCYCLE as Virtual Machine Life Cycle Manager. After a tuning activity that required the optimization of severalaspects included Image distribution and Network configuration, we wer able to run production jobs over the different Cloud with success.
© 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.
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