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

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.

Keywords:

• Internet of vehicles
• small files correlation probability
• small files merging scheme
• prefetching and caching strategy

Acknowledgments

Special thanks go to anonymous reviewers for their great suggestions.

Additional information

Funding

This research was supported by the Soft Science Research of Hubei Province (NO.2019ADC071), the Natural Science Foundation of Hubei Province (NO. 2016CFB650), the National Natural Science Foundation of China (NO. 61772562), and the Hubei Provincial Natural Science Foundation of China for Distinguished Young Scholars (NO. 2017CFA043), and Fundamental Research Funds for the Central Universities (CZP19004), and Youth Elite Project of State Ethnic Affairs Commission of China.