Prefetching Cache Optimization Using Graph Neural Networks: A Modular Framework and Conceptual Analysis
This work addresses the performance gap in computing systems for applications like web browsing and file management, but it is incremental as it builds on existing GNN techniques for a specific domain.
The paper tackles the problem of improving prefetching cache performance by addressing the limitations of traditional heuristics and statistical models in capturing complex data access patterns. It introduces a modular framework using Graph Neural Networks (GNNs) to model graph-structured data, such as web navigation and file systems, and provides a conceptual analysis showing that GNN-based approaches can outperform conventional methods.
Caching and prefetching techniques are fundamental to modern computing, serving to bridge the growing performance gap between processors and memory. Traditional prefetching strategies are often limited by their reliance on predefined heuristics or simplified statistical models, which fail to capture the complex, non-linear dependencies in modern data access patterns. This paper introduces a modular framework leveraging Graph Neural Networks (GNNs) to model and predict access patterns within graph-structured data, focusing on web navigation and hierarchical file systems. The toolchain consists of: a route mapper for extracting structural information, a graph constructor for creating graph representations, a walk session generator for simulating user behaviors, and a gnn prefetch module for training and inference. We provide a detailed conceptual analysis showing how GNN-based approaches can outperform conventional methods by learning intricate dependencies. This work offers both theoretical foundations and a practical, replicable pipeline for future research in graph-driven systems optimization.