#4365. A machine learning assisted data placement mechanism for hybrid storage systems

September 2026publication date
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Journal’s subject area:
Communication
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Abstract:
Emerging applications produce massive files that show different properties in file size, lifetime, and read and write frequency. Existing hybrid storage systems place these files onto different storage mediums assuming that the access patterns of files are fixed. The key to improve the file access performance is to adaptively place the files on the hybrid storage system using the run-time status and the properties of both files and the storage systems. In this paper, we propose a machine learning assisted data placement mechanism that adaptively places files onto the proper storage medium by predicting access patterns of files. Based on data access prediction results, we present a linear data placement algorithm to optimize the data access performance on the hybrid storage mediums. Extensive experimental results show that the proposed learning algorithm can achieve over 90% accuracy for predicting file access patterns.
Keywords:
Data placement; Hybrid storage; Machine learning

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