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PoS - Proceedings of Science
Volume 415 - International Symposium on Grids & Clouds 2022 (ISGC2022) - Physics (including HEP) and Engineering Applications Session
Exploiting INFN-Cloud to implement a Cloud solution to support the CYGNO computing model
F.D. Amaro, M. Antonacci, E. Baracchini, L. Benussi, S. Bianco, C. Capoccia, M. Caponero, D.S. Cardoso, G. Cavoto, D. Ciangottini, A. Cortez, I.A. Costa, I.A. Costa*, G. D'Imperio, E. Dané, G. Dho, F. Di Giambattista, E. Di Marco, C. Duma, F. Iacoangeli, H.P. Lima Júnior, G.S.P. Lopes, G. Maccarrone, R.D.P. Mano, M. Marafini, R.R. Marcelo Gregorio, D.J.G. Marques, G. Mazzitelli, A.G. McLean, A. Messina, C.M.B. Monteiro, R.A. Nobrega, I.F. Pains, E. Paoletti, L. Passamonti, S. Pelosi, F. Petrucci, S. Piacentini, D. Piccolo, D. Pierluigi, D. Pinci, A. Prajapati, F. Renga, A. Rodano, R.J.C. Roque, F. Rosatelli, A. Russo, G. Saviano, D. Spiga, N.J.C. Spooner, S. Stanlio, R. Tesauro, S. Tomassini, S. Torelli, M. Tracolli and J.M.F. dos Santoset al. (click to show)
Full text: pdf
Published on: September 28, 2022
Abstract
The aim of the CYGNO project is to demonstrate the capability of a high resolution gaseous TPC based on sCMOS (scientific CMOS) optical readout for present and future directional Dark Matter searches at low WIMP masses (1-10 GeV) down to and beyond the Neutrino Floor.
CYGNO is a medium-size astroparticle physics experiment that requires a relatively small amount of computing resources and for this reason can be subjected to a fragmentation and low utilization rate. This is a typical use case that could exploit and benefit from all the features of a Cloud infrastructure.
In the context of the INFN Cloud project, a container-based system has been developed in order to provide a seamless integration between storage and computing system. The latter is based on JupyterHub to provide a multi user server to access the experiment environment (ROOT, GEANT, GARFIELD++, libraries, etc). The token based authentication and authorization system allows a seamless integration with S3 Cloud Storage where a remote DAQ system continuously uploads acquired files. The result is a "Software as a Service" (SaaS) layer for data analysis and simulation with common tools of our community.
The paper will detail the overall project and preliminary user experiences.
DOI: https://doi.org/10.22323/1.415.0021
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