Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking †
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College of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China
2
Jiangsu Province Big Data Ubiquitous Perception and Intelligent Agricultural Application Engineering Research Center, Zhenjiang 212013, China
3
Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace, Zhenjiang 212013, China
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in Peng, E.; Li, Z. Optimal control-based computing task scheduling in software-defined vehicular edge networks. In Proceedings of the IEEE International Conference on Internet of Things and Intelligent Applications (ITIA), Zhenjiang, China, 27–29 November, 2020.
Academic Editor: Lei Shu
Sensors 2021, 21(3), 955; https://doi.org/10.3390/s21030955
Received: 5 January 2021
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Revised: 27 January 2021
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Accepted: 28 January 2021
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Published: 1 February 2021
(This article belongs to the Special Issue Selected Papers from the International Conference on Internet of Things and Intelligent Applications (ITIA2020))
With the development of smart vehicles and various vehicular applications, Vehicular Edge Computing (VEC) paradigm has attracted from academic and industry. Compared with the cloud computing platform, VEC has several new features, such as the higher network bandwidth and the lower transmission delay. Recently, vehicular computation-intensive task offloading has become a new research field for the vehicular edge computing networks. However, dynamic network topology and the bursty computation tasks offloading, which causes to the computation load unbalancing for the VEC networking. To solve this issue, this paper proposed an optimal control-based computing task scheduling algorithm. Then, we introduce software defined networking/OpenFlow framework to build a software-defined vehicular edge networking structure. The proposed algorithm can obtain global optimum results and achieve the load-balancing by the virtue of the global load status information. Besides, the proposed algorithm has strong adaptiveness in dynamic network environments by automatic parameter tuning. Experimental results show that the proposed algorithm can effectively improve the utilization of computation resources and meet the requirements of computation and transmission delay for various vehicular tasks.
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Keywords:
software-defined vehicular edge networking; resource allocation; computation task scheduling; optimal control
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MDPI and ACS Style
Li, Z.; Peng, E. Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking. Sensors 2021, 21, 955. https://doi.org/10.3390/s21030955
AMA Style
Li Z, Peng E. Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking. Sensors. 2021; 21(3):955. https://doi.org/10.3390/s21030955
Chicago/Turabian StyleLi, Zhiyuan, and Ershuai Peng. 2021. "Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking" Sensors 21, no. 3: 955. https://doi.org/10.3390/s21030955
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