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Data prefetching is important for storage system optimization and accessperformance improvement. Traditional prefetchers work well for mining accesspatterns of sequential logical block address (LBA) but cannot handle complexnon-sequential patterns that commonly exist in real-world applications. Thestate-of-the-art (SOTA) learning-based prefetchers cover more LBA accesses.However, they do not adequately consider the spatial interdependencies betweenLBA deltas, which leads to limited performance and robustness. This paperproposes a novel Stream-Graph neural network-based Data Prefetcher (SGDP).Specifically, SGDP models LBA delta streams using a weighted directed graphstructure to represent interactive relations among LBA deltas and furtherextracts hybrid features by graph neural networks for data prefetching. Weconduct extensive experiments on eight real-world datasets. Empirical resultsverify that SGDP outperforms the SOTA methods in terms of the hit ratio by6.21%, the effective prefetching ratio by 7.00%, and speeds up inference timeby 3.13X on average. Besides, we generalize SGDP to different variants bydifferent stream constructions, further expanding its application scenarios anddemonstrating its robustness. SGDP offers a novel data prefetching solution andhas been verified in commercial hybrid storage systems in the experimentalphase. Our codes and appendix are available athttps://github.com/yyysjz1997/SGDP/.

SGDP: A Stream-Graph Neural Network Based Data Prefetcher