Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150757
Title: Storage and access optimization scheme based on correlation probabilities in the internet of vehicles
Authors: Bin, Zhou
Yao, Yuhao
Liu, Xiao
Zhu, Rongbo
Sangaiah, Arun Kumar
Ma, Maode
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Bin, Z., Yao, Y., Liu, X., Zhu, R., Sangaiah, A. K. & Ma, M. (2020). Storage and access optimization scheme based on correlation probabilities in the internet of vehicles. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 24(3), 221-236. https://dx.doi.org/10.1080/15472450.2019.1612247
Journal: Journal of Intelligent Transportation Systems: Technology, Planning, and Operations
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.
URI: https://hdl.handle.net/10356/150757
ISSN: 1547-2450
DOI: 10.1080/15472450.2019.1612247
Rights: © 2019 Taylor & Francis Group, LLC. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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