Smart caching in a Data Lake for High Energy Physics analysis
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 42 references that are in the map. First posted on arXiv 2 Aug 2022.
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5 of 42 references.
References · 5
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
- 2018Competitive caching with machine learned advicecited by 195
- 2015Deep Reinforcement Learning in Large Discrete Action Spacescited by 161
- 2019RLCache: Automated Cache Management Using Reinforcement Learningcited by 2
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