Storage and access optimization scheme based on correlation probabilities in the internet of vehicles
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 gen...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/150757 |
| _version_ | 1826116341623422976 |
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| author | Bin, Zhou Yao, Yuhao Liu, Xiao Zhu, Rongbo Sangaiah, Arun Kumar Ma, Maode |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Bin, Zhou Yao, Yuhao Liu, Xiao Zhu, Rongbo Sangaiah, Arun Kumar Ma, Maode |
| author_sort | Bin, Zhou |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T04:10:07Z |
| format | Journal Article |
| id | ntu-10356/150757 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:10:07Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1507572021-08-02T01:42:51Z Storage and access optimization scheme based on correlation probabilities in the internet of vehicles Bin, Zhou Yao, Yuhao Liu, Xiao Zhu, Rongbo Sangaiah, Arun Kumar Ma, Maode School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Internet of Vehicles Small Files Correlation Probability 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. 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. 2021-08-02T01:42:51Z 2021-08-02T01:42:51Z 2020 Journal Article 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 1547-2450 https://hdl.handle.net/10356/150757 10.1080/15472450.2019.1612247 2-s2.0-85065998094 3 24 221 236 en Journal of Intelligent Transportation Systems: Technology, Planning, and Operations © 2019 Taylor & Francis Group, LLC. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering Internet of Vehicles Small Files Correlation Probability Bin, Zhou Yao, Yuhao Liu, Xiao Zhu, Rongbo Sangaiah, Arun Kumar Ma, Maode Storage and access optimization scheme based on correlation probabilities in the internet of vehicles |
| title | Storage and access optimization scheme based on correlation probabilities in the internet of vehicles |
| title_full | Storage and access optimization scheme based on correlation probabilities in the internet of vehicles |
| title_fullStr | Storage and access optimization scheme based on correlation probabilities in the internet of vehicles |
| title_full_unstemmed | Storage and access optimization scheme based on correlation probabilities in the internet of vehicles |
| title_short | Storage and access optimization scheme based on correlation probabilities in the internet of vehicles |
| title_sort | storage and access optimization scheme based on correlation probabilities in the internet of vehicles |
| topic | Engineering::Electrical and electronic engineering Internet of Vehicles Small Files Correlation Probability |
| url | https://hdl.handle.net/10356/150757 |
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