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

Abstract

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 solidstate 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×, on average, compared to the state-of-the-art TPFTL scheme.