The Russian Segment (RU-VRF) in WLCG Infrastructure: High Performance Computing Network
The paper describes a high-performance computer network optimized for the transmission of experimental data generated by CERN for potential use by the Large Hadron Collider (LHC) network community, including the Russian segment of this network. The Worldwide LHC Computing Grid (WLCG) is a global com...
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Published in: | 2022 International Conference on Modern Network Technologies (MoNeTec) pp. 1 - 4 |
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Main Authors: | , , , , , |
Format: | Conference Proceeding |
Language: | English |
Published: |
IEEE
27-10-2022
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Subjects: | |
Online Access: | Get full text |
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Summary: | The paper describes a high-performance computer network optimized for the transmission of experimental data generated by CERN for potential use by the Large Hadron Collider (LHC) network community, including the Russian segment of this network. The Worldwide LHC Computing Grid (WLCG) is a global computing infrastructure whose mission is to provide computing resources for storing, distributing and analyzing data generated by the LHC at CERN, making the data equally accessible to all participants, regardless of their physical location. WLCG is a multi-layer distributed computing infrastructure. It provides a community of over 12,200 scientists of 110 nationalities from institutions in over 40 countries with near real-time access to LHC data. WLCG is the world's largest computer network. It is supported by many related national and international networks and projects around the world. Since the beginning of the WLCG Project the Russian computing centers in NRC "Kurchatov Institute" an JINR have participated with reliable and stable resources as well as personnel for the LHC. Contribution to the ALICE, ATLAS, CMS and LHCb Tier 1 and Tier 2 computing resources (disk and CPUs) in the last 10 years has been substantial. In this paper, we would like to show the evolution and technical updates in the Russian segment Tier1 and Tier 2. The Russian segment is an infrastructure fully integrated into the WLCG. |
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DOI: | 10.1109/MoNeTec55448.2022.9960772 |