In the new millennium, a myriad of large‐scale applications (e.g., social networks, ecommerce, internet of things, and scientific experiments) often generate large volumes of data. Given their volumes, their heterogeneous and distributed nature, the management of such data constitutes a challenge for distributed systems, particularly cloud computing. In this regard, data replication is a well‐known and effective data management technique that consists in creating multiple copies of the same data in different storage resources. In this article, we propose a popularity and correlation based data replication strategy, called PCDR. The main idea of our strategy is to replicate a set of the most popular correlated file groups based on files access history analysis. In this respect, the popularity of the files in each group is determined while taking into consideration temporal locality. Moreover, a clustering technique is used to find groups of correlated files. Using the CloudSim simulator, extensive experimentations show that our proposed strategy outperforms other strategies for several evaluation metrics.

中文翻译：

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基于半衰期概念和集群的云系统中的流行度和相关性感知数据复制策略

在新的千年中，大量的大型应用程序（例如，社交网络，电子商务，物联网和科学实验）通常会生成大量数据。考虑到它们的数量，它们的异构性和分布式性质，对此类数据的管理对分布式系统（尤其是云计算）构成了挑战。在这方面，数据复制是一种众所周知的有效数据管理技术，它包括在不同的存储资源中创建同一数据的多个副本。在本文中，我们提出了一种基于流行度和相关性的数据复制策略，称为PCDR。我们策略的主要思想是基于文件访问历史分析来复制一组最受欢迎的相关文件组。在这方面，在考虑时间局部性的同时确定每个组中文件的受欢迎程度。此外，使用聚类技术来找到相关文件的组。使用CloudSim模拟器进行的大量实验表明，对于几种评估指标，我们提出的策略要优于其他策略。