  Data prefetching is important for storage system optimization and access
performance improvement. Traditional prefetchers work well for mining access
patterns of sequential logical block address (LBA) but cannot handle complex
non-sequential patterns that commonly exist in real-world applications. The
state-of-the-art (SOTA) learning-based prefetchers cover more LBA accesses.
However, they do not adequately consider the spatial interdependencies between
LBA deltas, which leads to limited performance and robustness. This paper
proposes a novel Stream-Graph neural network-based Data Prefetcher (SGDP).
Specifically, SGDP models LBA delta streams using a weighted directed graph
structure to represent interactive relations among LBA deltas and further
extracts hybrid features by graph neural networks for data prefetching. We
conduct extensive experiments on eight real-world datasets. Empirical results
verify that SGDP outperforms the SOTA methods in terms of the hit ratio by
6.21%, the effective prefetching ratio by 7.00%, and speeds up inference time
by 3.13X on average. Besides, we generalize SGDP to different variants by
different stream constructions, further expanding its application scenarios and
demonstrating its robustness. SGDP offers a novel data prefetching solution and
has been verified in commercial hybrid storage systems in the experimental
phase. Our codes and appendix are available at
https://github.com/yyysjz1997/SGDP/.
