RARO: Reliability-aware Conversion with Enhanced Read Performance for QLC SSDs

TL;DR

RARO is a hybrid flash management scheme that enhances QLC SSD read performance by intelligently migrating hot data based on read retries, reducing capacity overhead and optimizing I/O performance.

Contribution

RARO introduces a reliability-aware data migration strategy that leverages real-time read retry statistics to improve read performance with minimal capacity overhead in QLC SSDs.

Findings

Significantly improves read performance across workloads

Reduces unnecessary mode conversions and capacity loss

Maintains negligible impact on usable capacity

Abstract

Quad-level cell (QLC) flash offers significant benefits in cost and capacity, but its limited reliability leads to frequent read retries, which severely degrade read performance. A common strategy in high-density flash storage is to program selected blocks in a low-density mode (SLC), sacrificing some capacity to achieve higher I/O performance. This hybrid storage architecture has been widely adopted in consumer-grade storage systems. However, existing hybrid storage schemes typically focus on write performance and rely solely on data temperature for migration decisions. This often results in excessive mode switching, causing substantial capacity overhead. In this paper, we present RARO (Reliability-Aware Read performance Optimization), a hybrid flash management scheme designed to improve read performance with minimal capacity cost. The key insight behind RARO is that much of the read slowdown in QLC flash is caused by read retries. RARO triggers data migration only when hot data resides in QLC blocks experiencing a high number of read retries, significantly reducing unnecessary conversions and capacity loss. Moreover, RARO supports fine-grained multi-mode conversions (SLC-TLC-QLC) to further minimize capacity overhead. By leveraging real-time read retry statistics and flash characteristics, RARO mitigates over-conversion and optimizes I/O performance. Experiments on the FEMU platform demonstrate that RARO significantly improves read performance across diverse workloads, with negligible impact on usable capacity.