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    Graph4Cache:一种用于缓存预取的图神经网络模型

    Graph4Cache: A Graph Neural Network Model for Cache Prefetching

    • 摘要: 大多数计算系统利用缓存来减少数据访问时间,加快数据处理并平衡服务负载. 缓存管理的关键在于确定即将被加载到缓存中或从缓存中丢弃的合适数据,以及进行缓存置换的合适时机,这对于提高缓存命中率至关重要. 现有的缓存方案面临2个问题:在实时的、在线的缓存场景下难以洞察用户访问数据的热度信息,以及忽略了数据访问序列之间复杂的高阶信息. 提出了一个基于GNN的缓存预取网络Graph4Cache.通过将单个访问序列建模为有向图(ASGraph),并引入虚拟节点聚合图中所有节点的信息和表示整个序列. 然后由ASGraph的虚拟节点构造一个跨序列无向图(CSGraph)来学习跨序列特征,这极大地丰富了单个序列中有限的数据项转换模式. 通过融合这2种图结构的信息,学习到了序列之间的高阶关联信息,并获取了丰富的用户意图. 在多个公共数据集上的实验结果证明了该方法的有效性. Graph4Cache在P@20和MRR@20上均优于现有的缓存预测算法.

       

      Abstract: 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.

       

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