Smart caching in a Data Lake for High Energy Physics analysis

TL;DR

This paper proposes an autonomous reinforcement learning-based caching management method for Data Lakes in High Energy Physics, aiming to enhance user experience and reduce infrastructure costs amidst growing data volumes.

Contribution

It introduces a novel reinforcement learning approach for autonomous data caching management in distributed Data Lake environments for High Energy Physics.

Findings

Improved data access efficiency in Data Lakes.

Reduced maintenance costs through autonomous caching.

Enhanced user experience in data retrieval.

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.