LinnOS: Predictability on Unpredictable Flash Storage with a Light Neural Network


Mingzhe Hao, Levent Toksoz, and Nanqinqin Li, University of Chicago; Edward Edberg Halim, Surya University; Henry Hoffmann and Haryadi S. Gunawi, University of Chicago


This paper presents LinnOS, an operating system that leverages a light neural network for inferring SSD performance at a very fine — per-IO — granularity and helps parallel storage applications achieve performance predictability. LinnOS supports black-box devices and real production traces without requiring any extra input from users, while outperforming industrial mechanisms and other approaches. Our evaluation shows that, compared to hedging and heuristic-based methods, LinnOS improves the average I/O latencies by 9.6-79.6% with 87-97% inference accuracy and 4-6μs inference overhead for each I/O, demonstrating that it is possible to incorporate machine learning inside operating systems for real-time decision-making.

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@inproceedings {258894,
author = {Mingzhe Hao and Levent Toksoz and Nanqinqin Li and Edward Edberg Halim and Henry Hoffmann and Haryadi S. Gunawi},
title = {{LinnOS}: Predictability on Unpredictable Flash Storage with a Light Neural Network},
booktitle = {14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20)},
year = {2020},
isbn = {978-1-939133-19-9},
pages = {173--190},
url = {},
publisher = {USENIX Association},
month = nov

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