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工程科学与技术:2024,56(1):89-98
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面向车辆边缘计算的任务合作卸载
(1.南京邮电大学 计算机学院,江苏 南京 210023;2.江苏省大数据安全与智能处理重点实验室,江苏 南京 210023;3.国铁吉讯科技有限公司,北京 100081)
Task Cooperative Offloading for Vehicle Edge Computing
(1.School of Computer Sci., Nanjing Univ. of Posts and Telecommunications, Nanjing 210023, China;2.Jiangsu Key Lab. of Big Data Security and Intelligent Processing, Nanjing 210023, China;3.China Railway Gecent Technol. Co., Ltd., Beijing 100081, China)
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本文已被:浏览 200次   下载 135
投稿时间:2022-09-05    
中文摘要: 随着物联网技术和人工智能技术的飞速发展,车辆边缘计算越来越引起学者的关注。车辆如何有效地利用其周边的各种通信、计算和缓存资源,结合边缘计算系统模型将计算任务迁移到离车辆更近的路边单元,已经成为目前车联网研究的热点。由于车辆应用设备计算资源的有限性,车辆用户的任务计算需求无法满足,需要提升车辆周边计算资源的利用率来完成计算任务。本文研究了车辆边缘计算中任务的合作卸载机制,以最小化车辆任务的计算时延。首先,考虑周边停泊车辆以及路边单元的计算资源,设计了由云服务器层、停泊车辆合作集群层和路边单元合作集群层组成的任务合作卸载3层系统架构,通过路边单元合作集群和停泊车辆合作集群的合作卸载,充分利用系统的空闲计算资源,进一步提高了系统的资源利用率。然后,基于k-聚类算法的思想提出了路边单元合作集群划分算法对路边单元进行合作集群的划分,并采用块连续上界最小化的分布式迭代优化方法设计了任务合作卸载算法,对终端车辆用户的任务进行卸载计算。最后,通过将本文算法和其他算法方案进行实验仿真对比,仿真结果表明,本文算法在系统时延和系统吞吐量方面具有更好的性能表现,可以降低23%的系统时延,并且能提升28%的系统吞吐量。
Abstract:With the rapid development of IoT technology and artificial intelligence technology, vehicle edge computing has attracted more and more attention. Effectively utilizing the various communication, computational and caching resources in the vicinity of vehicles, and employing edge computing system models to migrate computational tasks closer to the vehicles, have become a hotspot in current Internet of Vehicles research. Due to the limited computational resources of in-vehicle devices, the computational demands of vehicle users cannot be met without making full use of the computational resources available in the vicinity of vehicles. Aiming to minimize the computational latency of vehicular tasks, a collaborative offloading mechanism for computational tasks in vehicle edge computing was investigated in this paper. Firstly, a three-layer architecture for task collaborative offloading was designed considering the computational resources of parked vehicles in the vicinity of vehicles as well as the computational resources of roadside units, which was comprised with three tiers: cloud server layer, roadside unit collaboration cluster layer, and the parked vehicle collaboration cluster layer. By means of collaborative offloading between the roadside unit collaboration cluster and the parked vehicle collaboration cluster, the free computational resources of system were fully leveraged, which further enhanced resource utilization. Then, in order to segment roadside units into collaboration clusters, a roadside unit collaboration cluster partitioning algorithm based on k-means clustering algorithm was proposed. A distributed iterative optimization approach with block-coordinate upper-bound minimization was utilized to design a task collaborative offloading algorithm for offloading the computation of terminal vehicle users' tasks. Finally, by comparing with other algorithm schemes through experiments, the algorithm proposed in this paper has better performance in terms of system latency and system throughput according to the stimulation result. Specifically, the system latency was reduced by 23% and the system throughput was increased by 28%.
文章编号:202200955     中图分类号:TP393.1    文献标志码:
基金项目:国家自然科学基金项目(62372249;62072254;62272237;62171217;62372250;62302236);中国铁道科学研究院集团有限公司院基金课题重点项目(2022YJ302)
作者简介:第一作者:鲁蔚锋(1979-),男,副教授,博士生.研究方向:边缘计算与网络安全.E-mail:luwf@njupt.edu.cn;通信作者:徐佳,E-mail:xujia@njupt.edu.cn
引用文本:
鲁蔚锋,印文徐,王菁,费汉明,徐佳.面向车辆边缘计算的任务合作卸载[J].工程科学与技术,2024,56(1):89-98.
LU Weifeng,YIN Wenxu,WANG Jing,FEI Hanming,XU Jia.Task Cooperative Offloading for Vehicle Edge Computing[J].Advanced Engineering Sciences,2024,56(1):89-98.