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Specific informations of {{{1}}}'s site:
- [[{{{1}}}:Hardware|Hardware]]
- [[{{{1}}}:Network|Network]]
- [[{{{1}}}:Storage|Storage]]
- [[{{{1}}}:External access|External access]]
- [[{{{1}}}:People|People]]
- Administration
Direct access to resources for {{{1}}}'s site:
- reservation state (monika) and reservation history (drawgantt) via OAR
- available resources via Ganglia
- critical services via Nagios
- opened bugs via BugZilla
- support-staff via mail
Shortcuts to global tools and informations:
- support procedures to report bugs or ask for enhancements
- global reservation state and global reservation history via OARgrid.
- registered users via UMS
Latest updated publications Five random publications that benefited from Grid'5000 (at least 3000 overall):
- Cherif Latreche, Nikos Parlavantzas, Hector A Duran-Limon. Do We Need Reinforcement Learning for Serverless Scheduling at the Edge? A Comparative Evaluation. 25th IEEE International Symposium on Parallel and Distributed Computing (ISPDC 2026), Jul 2026, Hambourg, Germany. hal-05626844v2 view on HAL pdf
- Pierre Epron, Gaël Guibon, Miguel Couceiro. ORPAILLEUR SyNaLP at CLEF 2024 Task 2: Good Old Cross Validation for Large Language Models Yields the Best Humorous Detection. Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2024), Sep 2024, Grenoble, France. pp.1841-1856. hal-04696012 view on HAL pdf
- Matthieu Simonin, Anne-Cécile Orgerie. Méthodologies de calcul d'empreinte carbone sur une plateforme de calcul : exemple du site Grid'5000 de Rennes. JRES 2024 - Journées réseaux de l'enseignement et de la recherche, Renater, Dec 2024, Rennes, France. pp.1-14. hal-04893984 view on HAL pdf
- Pierre Jacquet, Maxime Agusti, Eddy Caron, Camille Coti, Marcos Dias de Assunção, et al.. Untangling GPU Power Consumption: Job-Level Inference in Cloud Shared Settings. EUROSYS 2026 - European Conference on Computer Systems, ACM, Apr 2026, Edinbourg, Ecosse, United Kingdom. pp.624-640, 10.1145/3767295.3769333. hal-05291033 view on HAL pdf
- Cédric Prigent. Towards Efficient and Trustworthy Federated Learning on the Computing Continuum. Machine Learning cs.LG. INSA de Rennes, 2025. English. NNT : 2025ISAR0003. tel-05279213 view on HAL pdf
Latest updated experiments {{#experiments: 5|{{{1}}}}}
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