@inproceedings{aad250de5b2c4bb2a23765d48be6f8e1,
title = "SFP: Smart File-Aware Prefetching for Flash based Storage Systems",
abstract = "Currently, most of the Flash-based storage systems reduce the performance gap between the main memory and storage by data prefetching. However, conventional prefetching techniques perform well on hard disk drives but have limited effectiveness and efficiency on Flash. It is because the complicate data access patterns in modern systems have not been well considered. In this paper, we propose SFP, a smart file-aware prefetching scheme for Flash-based storage systems. SFP demonstrates that prefetching accuracy and efficiency can be improved comprehensively in a file-aware approach. Furthermore, three schemes are proposed: file access pattern learning, dynamic window-based file prefetching, and learning model size optimization. Experiments on the real server show that SFP reduces the access latency by up to 40\% compared with the state-of-the-art with low memory and computation cost.",
keywords = "file prefetching, flash, storage system",
author = "Han Wang and Longfei Luo and Liang Shi and Changlong Li and Xue, \{Chun Jason\} and Qingfeng Zhuge and Sha, \{Edwin H.-M.\}",
year = "2021",
month = jun,
doi = "10.1145/3453688.3461492",
language = "English",
series = "Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI",
publisher = "Association for Computing Machinery",
pages = "45--50",
booktitle = "GLSVLSI '21",
address = "United States",
note = "31st Great Lakes Symposium on VLSI (GLSVLSI 2021) : “Machine learning in CAD, EDA, and VLSI Design and Security” ; Conference date: 22-06-2021 Through 25-06-2021",
}