Abstract
The continuous growth of data production in almost all scientific areas raises new problems in data access and management, especially in a scenario where the end-users, as well as the resources that they can access, are worldwide distributed. This work is focused on the data caching management in a Data Lake infrastructure in the context of the High Energy Physics field. We are proposing an autonomous method, based on Reinforcement Learning techniques, to improve the user experience and to contain the maintenance costs of the infrastructure.
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The authors thank the CMS collaboration, and in particular the Machine Learning and Offline Software and Computing groups for the valuable discussions that helped the development of this work.
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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Open access funding provided by Universitá degli Studi di Perugia within the CRUI-CARE Agreement.
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T.T. M.T. L.S. D.S. wrote the manuscript text and prepared figures. All authors reviewed the manuscript.
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Tedeschi, T., Baioletti, M., Ciangottini, D. et al. Smart Caching in a Data Lake for High Energy Physics Analysis. J Grid Computing 21, 42 (2023). https://doi.org/10.1007/s10723-023-09664-z
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DOI: https://doi.org/10.1007/s10723-023-09664-z