智慧交通场景下云边端协同的多目标优化卸载决策

doi:10.19665/j.issn1001-2400.20230802

西安电子科技大学学报 ›› 2024, Vol. 51 ›› Issue (3): 63-75.doi: 10.19665/j.issn1001-2400.20230802

智慧交通场景下云边端协同的多目标优化卸载决策

朱思峰¹(), 宋兆威¹(), 陈昊¹(), 朱海²(), 乔蕊²()

1.天津城建大学计算机与信息工程学院,天津 300384
2.周口师范学院网络工程学院,河南周口 466001

收稿日期:2023-04-21 出版日期:2024-06-20 发布日期:2023-09-21
通讯作者: 宋兆威(1999—),男,天津城建大学硕士研究生,E-mail:szw9992022@163.com
作者简介:朱思峰(1975—),男,教授,E-mail:zhusifeng@163.com
陈昊(1982—),男,副教授,E-mail:chenhao@tcu.edu.cn
朱海(1978—),男,教授,E-mail:zhu_sea@163.com
乔蕊(1982—),女,副教授,E-mail:18033023@qq.com
基金资助:
国家自然科学基金(62172457);天津市自然科学基金重点项目(22JCZDJC00600);河南省高校科技创新人才支持计划(23HASTIT029)

Multi-objective optimization offloading decision with cloud-side-end collaboration in smart transportation scenarios

ZHU Sifeng¹(), SONG Zhaowei¹(), CHEN Hao¹(), ZHU Hai²(), QIAO Rui²()

1. School of Computer and Information Engineering,Tianjin Chengjian University,Tianjin 300384 China
2. School of Network Engineering,Zhoukou Normal University,Zhoukou 466001,China

Received:2023-04-21 Online:2024-06-20 Published:2023-09-21

摘要/Abstract

摘要：

随着智慧交通、云计算网络以及边缘计算网络的快速发展,车载终端与路基单元、中心云服务器之间的信息交互变得越发频繁。针对智慧交通云边端协同计算场景下如何高效地实现车路云一体化融合感知、群体决策以及各级服务器间对资源的合理分配问题,设计了基于云边端与智慧交通全面融合的网络架构。在该架构下,通过对任务类型的合理划分,再由各服务器对其进行选择性的缓存、卸载;在智慧交通云边端协同计算场景下,依次设计了一种对任务自适应的缓存模型、任务卸载时延模型、系统能量损耗模型、车载用户对服务质量不满意度评价模型、多目标优化问题模型,并给出了一种基于改进型非支配遗传算法的任务卸载决策方案。实验结果表明,文中方案能够有效降低任务卸载过程中所带来的时延和能耗,提高了系统资源利用率,给车辆用户带来更好的服务体验。

关键词: 智慧交通, 云边端协同计算, 卸载决策, 多目标优化算法, 非支配遗传算法

Abstract:

With the rapid development of intelligent transportation,the cloud computing network and the edge computing network,the information interaction among vehicle terminal,road base unit and central cloud server becomes more and more frequent.In view of how to efficiently realize vehicle-road-cloud integration fusion sensing,group decision making and reasonable allocation of re-sources between each server and the servers under the cloud-edge-terminal collaborative computing scenario of intelligent transportation,a network architecture based on the comprehensive convergence of the cloud-edge-terminal and intelligent transportation is designed.A network architecture based on the comprehensive integration of cloud-side-end and intelligent transportation is designed.Under this architecture,by reasonably dividing the task types,each server selectively caches and offloads them;under the collaborative computing scenario of the cloud-side-end of intelligent transportation,an adaptive caching model for tasks,a task offloading delay model,a system energy loss model,a model for evaluating the dissatisfaction of in-vehicle users with the quality of service,and a model for the multi-objective optimization problem are designed in turn,and a multi-objective optimization task offloading decision-making scheme is given based on the improved non-dominated genetic algorithms.Experimental results show that the proposed scheme can effectively reduce the delay and energy consumption brought by the task offloading process,improve the utilization rate of system resources,and bring better service experience to the vehicle user.

Key words: smart transportation, cloud edge collaborative computing, offloading decision, multi-objective optimization algorithm, non-dominated select genetic algorithms Ⅱ(NSGA-Ⅱ) algorithm

中图分类号:

TP393.1

朱思峰, 宋兆威, 陈昊, 朱海, 乔蕊. 智慧交通场景下云边端协同的多目标优化卸载决策[J]. 西安电子科技大学学报, 2024, 51(3): 63-75.

ZHU Sifeng, SONG Zhaowei, CHEN Hao, ZHU Hai, QIAO Rui. Multi-objective optimization offloading decision with cloud-side-end collaboration in smart transportation scenarios[J]. Journal of Xidian University, 2024, 51(3): 63-75.

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参考文献 22

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参数	数值
任务的数据量D_i_,_k/MIPS	1~5随机
存储任务所需的存储资源Z_i_,_k/MIPS	1~5随机
服务单元计算资源C_i/MIPS	10,100,600,1 000
缓存资源Z_r/MIPS	10~100随机
带宽B_M/(M·bit^-1)	200,300,2 000
信道个数N	10,6,5
边缘服务器传输功率P/W	600
同一信道下车辆之间的干扰参数L/Hz	60~120随机
边缘服务器与路测单元间的信道增益η/W	10~15随机
ɑ₁	0.8
ɑ₂	0.1
ɑ₃	0.8
θ_V2V/(M·bit^-1)	600
θ_V2E/(M·bit^-1)	1 000
智能网联车计算功率∂/W	50
智能网联车传输功率γ/W	100

智慧交通场景下云边端协同的多目标优化卸载决策

Multi-objective optimization offloading decision with cloud-side-end collaboration in smart transportation scenarios

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