Abstract Following the rapid development of the Internet of vehicles (IoV), many issues and challenges do come up as the storage of large quantities of vehicle network data and improvement of the retrieval efficiency. A great deal of global positioning system (GPS) log data and vehicle monitoring data is generated on IoV. When many small files in the conventional Hadoop Distributed File System (HDFS) are accessed, a series of problems arise such as high occupancy rate, low access efficiency and low retrieval efficiency, which lead to degrade the performance of IoV. In an attempt to tackle these bottleneck problems, a small Files Correlation Probability (FCP) model is proposed, which is based on the Text Feature Vector (TFV) presented in this paper. The Small Files Merge Scheme based on FCP (SFMS-FCP) and the Small File Prefetching and Caching Strategies (SFPCS) are proposed to optimize the storage and access performance of HDFS. Finally, experiments show that the proposed optimization solutions achieve better performance in terms of high occupancy of HDFS name nodes and low access efficiency, compared with the native HDFS read-write scheme and HAR-based read-write optimization scheme.

中文翻译：

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车联网中基于相关概率的存储访问优化方案

摘要 随着车联网（IoV）的快速发展，大量车联网数据的存储和检索效率的提高也带来了许多问题和挑战。大量的全球定位系统（GPS）日志数据和车辆监控数据是在车联网上产生的。传统Hadoop分布式文件系统（HDFS）中的大量小文件在访问时，会出现占用率高、访问效率低、检索效率低等一系列问题，导致车联网性能下降。为了解决这些瓶颈问题，提出了一种小型文件相关概率（FCP）模型，该模型基于本文中提出的文本特征向量（TFV）。提出了基于FCP的小文件合并方案（SFMS-FCP）和小文件预取和缓存策略（SFPCS）来优化HDFS的存储和访问性能。最后，实验表明，与原生HDFS读写方案和基于HAR的读写优化方案相比，所提出的优化方案在HDFS名称节点占用率高和访问效率低方面取得了更好的性能。