Reducing Solid-State Drive Read Latency by Optimizing Read-Retry
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Abstract (arXiv)
3D NAND flash memory with advanced multi-level cell techniques provides high storage density, but suffers from significant performance degradation due to a large number of read-retry operations. Although the read-retry mechanism is essential to ensuring the reliability of modern NAND flash memory, it can significantly increase the read latency of an SSD by introducing multiple retry steps that read the target page again with adjusted read-reference voltage values. Through a detailed analysis of the read mechanism and rigorous characterization of 160 real 3D NAND flash memory chips, we find new opportunities to reduce the read-retry latency by exploiting two advanced features widely adopted in modern NAND flash-based SSDs: 1) the CACHE READ command and 2) strong ECC engine. First, we can reduce the read-retry latency using the advanced CACHE READ command that allows a NAND flash chip to perform consecutive reads in a pipelined manner. Second, there exists a large ECC-capability margin in the final retry step that can be used for reducing the chip-level read latency. Based on our new findings, we develop two new techniques that effectively reduce the read-retry latency: 1) Pipelined Read-Retry (PR2) and 2) Adaptive Read-Retry (AR2). PR2 reduces the latency of a read-retry operation by pipelining consecutive retry steps using the CACHE READ command. AR2 shortens the latency of each retry step by dynamically reducing the chip-level read latency depending on the current operating conditions that determine the ECC-capability margin. Our evaluation using twelve real-world workloads shows that our proposal improves SSD response time by up to 31.5% (17% on average) over a state-of-the-art baseline with only small changes to the SSD controller.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Mar 2021.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 1.
3 papers.
- 2022Sibyl: Adaptive and Extensible Data Placement in Hybrid Storage Systems Using Online Reinforcement Learningcited by 8
- 2025Harmonia: Enhancing Data Placement and Migration in Hybrid Storage Systems via Multi-Agent Reinforcement Learningcited by 1
- 2026Co-Design of Memory-Storage Systems for Workload Awareness with Interpretable Modelscited by 0
- 2026Co-Design of Memory-Storage Systems for Workload Awareness with Interpretable Models16 Mar 2026
- 2025Harmonia: Enhancing Data Placement and Migration in Hybrid Storage Systems via Multi-Agent Reinforcement Learning26 Mar 2025
- 2022Sibyl: Adaptive and Extensible Data Placement in Hybrid Storage Systems Using Online Reinforcement Learning15 May 2022