In this study, we propose a caching method called RUE for dynamic large-scale data streams. We define a data model to facilitate hot data identification and management. At the heart of RUE model is hot degree that takes into account two factors data resource utilization efficiency and reuse distance, aiming to quantitatively reflect data popularity in a dynamic data stream. Based on data's hot degree, RUE classifies data into four types, each of which is assigned with an associated cache residence time. Guided by RUE model, we develop HM algorithm to identify and manage hot data in a dynamic data stream. HM algorithm is implemented by four stacks, namely, new stack, short stack, long stack, and temp stack. Moreover, an eviction and a migration algorithms are integrated into HM to facilitate block replacement and migration. To evaluate the performance of HM algorithm, we quantitatively compare the performance of RUE with three state-of-art algorithms, namely, LRU, LIRS, and ARC under various replacement policies, operations, and workloads. Experimental results show that RUE outperforms these three existing algorithms in terms of both read and write hit rates. Furthermore, we show that with the four stacks in place, the computing overhead of HM is negligible.

中文翻译：

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RUE：一种利用资源利用效率识别和管理热点数据的缓存方法

在这项研究中，我们为动态大规模数据流提出了一种称为RUE的缓存方法。我们定义了一个数据模型，以方便热点数据的识别和管理。RUE模型的核心是热度，它考虑了数据资源利用效率和复用距离两个因素，旨在定量反映动态数据流中的数据流行度。RUE根据数据的热度将数据分为四种类型，每种类型都分配有相关的缓存停留时间。在 RUE 模型的指导下，我们开发了HM算法来识别和管理动态数据流中的热点数据。HM算法由四个栈实现，分别是新栈、短栈、长栈和临时栈. 此外，将驱逐和迁移算法集成到 HM 中以促进块替换和迁移。为了评估 HM 算法的性能，我们定量比较了 RUE 与三种最先进算法（即 LRU、LIRS 和 ARC）在各种替换策略、操作和工作负载下的性能。实验结果表明，RUE 在读写命中率方面均优于这三种现有算法。此外，我们表明，在四个堆栈就位的情况下，HM 的计算开销可以忽略不计。