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GFPP: A confidence-aware file system prefetching method based on deep graph networks
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AI
DOI：10.1016/j.eswa.2026.132247.png)
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Abstract
En 中文
File system caching is crucial for enhancing I/O performance; yet traditional caching strategies exhibit low efficiency when handling complex, non-sequential, and data-intensive workloads characterized by concurrent multi-user access, as typified by AI and database applications. Existing learning-based prefetching research faces a dual challenge of mismatched prediction granularity and the absence of semantic information. This paper proposes GFPP (Graph-based File Prefetcher), a deep graph neural network framework operating at the file system level. GFPP innovatively employs a parallel spatio-temporal decoupled architecture: a Graph Neural Network (GNN) branch extracts microscopic spatial-topological structures from dynamic I/O interaction graphs, while a multi-scale temporal Convolutional Neural Network (CNN) branch concurrently captures macroscopic long-range sequential patterns; simultaneously integrating a Dynamic-K optimization mechanism. In large-scale real-world cloud platform I/O workloads, GFPP’s cache hit rate is significantly superior to advanced baseline models such as Transformer, GAT, and SGDP. Furthermore, in zero-shot generalization tests on Web application and database workloads, the strong adaptability and robustness of GFPP under unseen workloads are demonstrated. This research reveals the core value of fine-grained spatio-temporal collaborative modeling at the file system level, providing an effective framework and a novel perspective for building next-generation high-performance intelligent caching systems.
Keywords:
File system caching
Graph Neural Network
Prefetching
Spatio-temporal modeling
Deep learning
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W
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