车联网云边端协同计算场景下融合边缘缓存机制的多目标优化卸载决策  

Multi-objective Optimization Offloading Decision with Edge Caching Mechanism in the Scenario of Cloud Edge Collaborative Computingfor the Internet of Vehicles

作　　者：朱思峰 王钰 张宗辉 朱海 乔蕊[3] 陈国强[4] 李尚谊 ZHU Sifeng;WANG Yu;ZHANG Zonghui;ZHU Hai;QIAO Rui;CHEN Guoqiang;LI Shangyi(School of Computer and Information Engineering,Tianjin Chengjian University,Tianjin 300384,China;School of Computer,Henan University of Engineering,Zhengzhou 451191,China;School of Computer,Zhoukou Normal University,Zhoukou 466001,China;School of Computer and Information Engineering,Henan University,Kaifeng 475000,China;Ulster College,Shaanxi University of Science and Technology,Xi'an 710016,China)

机构地区：[1]天津城建大学计算机与信息工程学院,天津300384 [2]河南工程学院计算机学院,郑州451191 [3]周口师范学院计算机学院,周口466001 [4]河南大学计算机与信息工程学院,开封475004 [5]陕西科技大学阿尔斯特学院,西安710016

出　　处：《北方工业大学学报》2024年第5期18-29,共12页Journal of North China University of Technology

基　　金：国家自然科学基金项目(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项目(23HASTIT029);河南省科技攻关计划项目(242102210027)。

摘　　要：随着汽车技术和车载网络的发展,诞生了大量以提高驾驶安全性、旅行舒适性和车内娱乐性为目的的车载应用,云边端协同计算模式在满足低能耗、低负载和高服务质量的车联网应用需求时面临着巨大挑战。针对这一问题,本文构建了缓存模型、时延模型、负载模型、服务质量模型和多目标优化模型,提出了一种融合边缘缓存机制的车联网任务卸载方案,并进行了对比实验。实验结果表明本文提出的卸载方案在时延、能耗、负载和服务质量方面均优于前人的研究方案。With the development of automotive technology and in-vehicle networks,a large number of invehicle applications aiming at improving driving safety,traveling comfort and in-vehicle entertainment have been born,and the cloud-edge-end collaborative computing model is facing a great challenge to meet the demand of lowenergy,low-load and high-quality-of-service in-vehicle Internet of Things(IoT)applications.To address this problem,this paper constructs a caching model,a delay model,a load model,a quality of service model,and a multi-objective optimization model,and proposes an offloading scheme for Telematics tasks that incorporates an edge caching mechanism,and conducts comparative experiments.The experimental results show that the offloading scheme proposed in this paper outperforms the schemes in the literature in terms of delay,energy consumption,load and quality of service.

关 键 词：车联网 云边端协同计算 边缘缓存 卸载方案 多目标优化模型 

分 类 号：TP393.1[自动化与计算机技术—计算机应用技术]