We present LearnedFTL, which applies learned indexes to on-demand page-level flash translation layer (FTL) designs to improve the random read performance of flash-based solid-state drives (SSDs). The first of its kind, it minimizes the number of double reads induced by address translation in random read accesses. To apply the learned indexes to address translation, LearnedFTL proposes dynamic piece-wise regression to efficiently build learned indexes. LearnedFTL also exploits the unique feature of page relocation in SSD internal garbage collection (GC), and embeds the learned index training in GC, which can minimize additional delay on normal read and write operations. Additionally, LearnedFTL employs a bitmap prediction filter to guarantee the accuracy of learned indexes' predictions. With these designs, LearnedFTL considerably speeds up address translation while reducing the number of flash read accesses caused by the demand-based page-level FTL. Our benchmark-driven experiments on a FEMU-based prototype show that LearnedFTL reduces the 99th percentile tail latency by 4.8$\times$, on average, compared to the state-of-the-art TPFTL scheme.

翻译：我们提出LearnedFTL，将学习索引应用于按需页级闪存转换层（FTL）设计，以提升闪存固态硬盘（SSD）的随机读取性能。作为首创方案，它最大程度地减少了随机读取访问中由地址转换引起的双重读取次数。为了将学习索引应用于地址转换，LearnedFTL提出了动态分段回归方法，以高效构建学习索引。LearnedFTL还利用了SSD内部垃圾回收（GC）中页面重定位的独特特性，并将学习索引训练嵌入到GC过程中，从而最大程度地减少对正常读写操作的额外延迟。此外，LearnedFTL采用位图预测过滤器来保证学习索引预测的准确性。通过这些设计，LearnedFTL显著加速了地址转换，同时减少了由基于需求的页级FTL导致的闪存读取访问次数。我们在基于FEMU的原型上进行的基准驱动实验表明，与最先进的TPFTL方案相比，LearnedFTL将第99百分位尾部延迟平均降低了4.8倍。