Time Series Machine Learning Models for Precise SSD Access Latency Prediction

Abstract

Solid State Drives (SSDs) have become the dominant storage solution over the past few years. A key component of SSDs is the controller, which manages communication between the host and flash memory, optimizing data transfer speeds, integrity, and lifespan. However, modern SSDs function as closed boxes, as manufacturers do not disclose firmware and controller details. Meanwhile, read and write latencies are affected by various internal optimizations, such as wear-leveling and garbage collection, making precise latency prediction challenging. Existing approaches rely on trace-driven simulation or machine learning, but either (1) just classify operations into broad latency categories (e.g., fast or slow), including software stack overhead, or (2) make imprecise predictions while consuming significant system resources and time. For system simulation, latency predictions must be both fast and accurate, focusing solely on device-level delays excluding OS overhead, which is modeled separately. To tackle these challenges, this paper presents time series machine learning models to accurately predict hardware-only SSD latencies across diverse workloads. Our evaluation shows that the proposed model predicts 85%–95% of individual I/O latencies within a 10% error margin, outperforming existing simulators and ML models, which achieve only 6%–37% accuracy, while also providing 4×–255× speedups in prediction latency.