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原文传递 Storage and access optimization scheme based on correlation probabilities in the internet of vehicles

题名：	Storage and access optimization scheme based on correlation probabilities in the internet of vehicles
正文语种：	英文
作者：	Zhou Bin;Yuhao Yao;Xiao Liu;Rongbo Zhu;Arun Kumar Sangaiah;Maode Ma
作者单位：	South-Central University for Nationalities
关键词：	Internet of vehicles; small files correlation probability; small files merging scheme; prefetching and caching strategy
摘要：	Following the rapid development of the Internet of vehicles (loV), 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 loV. 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 loV. In an attempt to tackle these bottle neck 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. Fin ally, experiments show that the proposed optimization solutions achieve better performanee 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.
出版日期：	2020
出版年：	2020
期刊名称：	Journal of Intelligent Transportation Systems Technology Planning and Operations
卷：	Vol24
期：	No01-06
页码：	221-236