Using a random forest to predict quantized reuse distance in an SSD write buffer

Hyejin Cha

doi:10.1007/s00607-024-01343-5

2024-09-05

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Hyejin Cha

In Kee Kim

Taeseok Kim *

DOI：10.1007/s00607-024-01343-5

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摘要

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Efficient management of the write buffer in solid-state drives (SSDs) can be achieved by predicting future I/O request patterns using machine learning techniques. However, the computational demands posed by sophisticated approaches like deep learning remain significant, despite the increasing computational power of SSDs. This paper presents a novel approach to write buffer management that addresses these challenges. Our method employs a lightweight yet accurate random forest classifier to predict the forward reuse distances (FRDs) of I/O requests, indicating the likelihood of recurring identical I/O requests. Our key insight is that, rather than aiming for exact FRD predictions for future individual requests, we focus on identifying whether the predicted FRD exceeds the buffer size. With this insight, our method implements efficient buffer management operations, including bypassing the buffer storage when necessary. To achieve this, we introduce a banding method that quantizes FRDs according to the buffer size. This enables predictions at the band level, forming the foundation for a lightweight machine learning model. Subsequently, we assign high caching priority to write requests that are anticipated to have a short FRD band. Through extensive evaluations utilizing a simulator, we demonstrate that our method achieves results comparable to those of the optimal algorithm in terms of hit rate in most scenarios. Moreover, our approach outperforms state-of-the-art algorithms, which depend on past I/O reference patterns, by up to 27%.

Keyword:

Solid-state drives (SSD)

Random forest classifier

Predicting forward reuse distance

Write buffer management