G&L: An Attention-based Model for Improving Prefetching in Solid-state Drives

Solid-state drives (SSDs) have become the storage device for many personal computers and enterprise servers. However, the access speed of SSDs is still a bottleneck for computer systems. With prefetching, SSDs are able to predict future requests before moving data to a faster storage unit. Nevertheless, the interleaved access flow of applications poses challenges for existing prefetchers. We propose a model named G&L consisting of two components, namely a generative pre-training (GPT) prefetcher and a logical block address (LBA)-I/O size table. The GPT prefetcher applies a decoder-only framework with a self-attention mechanism to forecast the next LBA. The LBA-I/O size table maps LBAs to their most frequently appeared I/Osize for prediction. To evaluate the entire performance of the G&L model, we propose an algorithm to simulate the buffer when the I/O size changes dynamically. Experiments show that the accuracy, coverage, and f1 score of the GPT prefetcher surpass the baselines in most cases. The prediction accuracy of the LBA-I/O size table exceeds the baseline on all datasets. The G&L model's prefetching accuracy, coverage, and f1 score also outperform the baseline considering variable I/O size.