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; 宋兆威; 陈昊; 朱海; 乔蕊

doi:10.19665/j.issn1001-2400.20230802

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智慧交通场景下云边端协同的多目标优化卸载决策

信息与通信工程 | 更新时间：2024-07-23

- 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
- 西安电子科技大学学报 2024年51卷第3期页码：63-75
- 作者机构：
  
  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
- 作者简介：
  
  [ "朱思峰(1975—),男,教授,E-mail:[email protected]" ]
  宋兆威(1999—),男,天津城建大学硕士研究生,E-mail:[email protected]
  [ "陈昊(1982—),男,副教授,E-mail:[email protected]" ]
  [ "朱海(1978—),男,教授,E-mail:[email protected]" ]
  [ "乔蕊(1982—),女,副教授,E-mail:[email protected]" ]
- 基金信息：
  
  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)
- DOI：10.19665/j.issn1001-2400.20230802
  中图分类号： TP393.1;TN929.5
- 纸质出版日期：2024-06-20，
  
  网络出版日期：2023-09-21，
  
  收稿日期：2023-04-21，
- 稿件说明：
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朱思峰, 宋兆威, 陈昊, 等. 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.

Sifeng ZHU, Zhaowei SONG, Hao CHEN, et al. Multi-objective optimization offloading decision with cloud-side-end collaboration in smart transportation scenarios[J]. Journal of Xidian University, 2024,51(3):63-75.
朱思峰, 宋兆威, 陈昊, 等. 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. DOI： 10.19665/j.issn1001-2400.20230802.

Sifeng ZHU, Zhaowei SONG, Hao CHEN, et al. Multi-objective optimization offloading decision with cloud-side-end collaboration in smart transportation scenarios[J]. Journal of Xidian University, 2024,51(3):63-75. DOI： 10.19665/j.issn1001-2400.20230802.

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。

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.

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

Keywords

smart transportationcloud edge collaborative computingoffloading decisionmulti-objective optimization algorithmnon-dominated select genetic algorithms Ⅱ(NSGA-Ⅱ) algorithm

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