Abstract
Big data is usually processed in a decentralized computational environment with a number of distributed storage systems and processing facilities to enable both online and offline data analysis. In such a context, data access is fundamental to enhance processing efficiency as well as the user experience inspecting the data and the caching system is a solution widely adopted in many diverse domains. In this context, the optimization of cache management plays a central role to sustain the growing demand for data. In this article, we propose an autonomous approach based on a Reinforcement Learning technique to implement an agent to manage the file storing decisions. Moreover, we test the proposed method in a real context using the information on data analysis workflows of the CMS experiment at CERN.
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Tracolli, M., Baioletti, M., Poggioni, V., Spiga, D., on behalf of the CMS Collaboration. (2020). Caching Suggestions Using Reinforcement Learning. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12565. Springer, Cham. https://doi.org/10.1007/978-3-030-64583-0_57
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DOI: https://doi.org/10.1007/978-3-030-64583-0_57
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