Graph4Cache: A Graph Neural Network Model for Cache Prefetching

Most computing systems utilize caching to reduce data access latency, speed up data processing and balance service load. The key to cache management is to determine the appropriate data to be loaded into or discarded from the cache, as well as the appropriate timing for cache replacement, which is critical to improving cache hit rate.The existing caching schemes face with two problems: In real-time and online caching scenarios, it is difficult to discern the heat information of user access to data while ignoring the complex high-order information among data-access-sequences. In this paper, we propose a GNN-based cache prefetching network named Graph4Cache. We model a single access sequence into a directed graph (ASGraph), where virtual nodes are used to aggregate the features of all nodes in graph and represent the whole sequence. Then a cross sequence undirected graph (CSGraph) is constructed from the virtual nodes of ASGraphs to learn cross-sequence features, which greatly complements the limited item transitions in a single sequence. By fusing the information of these two graphs , we learn the high-order correlations among sequences and get abundant user intents. Experimental results on multiple public data sets demonstrate the effectiveness of this method. Graph4Cache outperforms the existing cache prediction algorithms on P@20 and MRR@20.