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

2025-08-26

AI Summary

QLC NAND flash suffers from low reliability, necessitating frequent read retries that severely degrade read performance; meanwhile, existing hybrid storage schemes primarily optimize writes and rely solely on data temperature for migration, causing excessive mode switching and capacity waste. This paper proposes a dynamic hybrid management scheme that jointly optimizes reliability and read performance. It introduces the first dual-factor migration trigger—integrating real-time read retry statistics with data temperature—to enable fine-grained, adaptive SLC/TLC/QLC mode conversion. Implemented on the FEMU platform, the scheme employs online retry monitoring and flash-specific reliability modeling to accurately identify high-retry hotspots and migrate them to higher-reliability modes. Experimental evaluation across diverse workloads demonstrates an average 32.7% improvement in read performance, while incurring less than 0.8% usable capacity loss.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Hardware-aware ML
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Optimizing read performance in QLC SSDs with minimal capacity cost
• Reducing unnecessary mode conversions caused by read retries
• Mitigating reliability issues while maintaining storage efficiency

Innovation

• Reliability-aware data migration for read performance
• Fine-grained multi-mode conversions to minimize capacity
• Real-time read retry statistics for optimization