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We present LearnedFTL, which applies learned indexes to on-demand page-levelflash translation layer (FTL) designs to improve the random read performance offlash-based solid-state drives (SSDs). The first of its kind, it minimizes thenumber of double reads induced by address translation in random read accesses.To apply the learned indexes to address translation, LearnedFTL proposesdynamic piece-wise regression to efficiently build learned indexes. LearnedFTLalso exploits the unique feature of page relocation in SSD internal garbagecollection (GC), and embeds the learned index training in GC, which canminimize additional delay on normal read and write operations. Additionally,LearnedFTL employs a bitmap prediction filter to guarantee the accuracy oflearned indexes' predictions. With these designs, LearnedFTL considerablyspeeds up address translation while reducing the number of flash read accessescaused by the demand-based page-level FTL. Our benchmark-driven experiments ona FEMU-based prototype show that LearnedFTL reduces the 99th percentile taillatency by 4.8$\times$, on average, compared to the state-of-the-art TPFTLscheme.

LearnedFTL: A Learning-based Page-level FTL for Improving Random Reads in Flash-based SSDs