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

This paper presents LinnOS, an operating system that lever-ages 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 4-6 mu s inference overhead for each I/O, demonstrating that it is possible to incorporate machine learning inside operating systems for real-time decision-making.