梁颖杰,,
崔亚平
1.重庆邮电大学通信与信息工程学院 重庆 400065
2.重庆高校市级光通信与网络重点实验室 重庆 400065
3.泛在感知与互联重庆市重点实验室 重庆 400065
基金项目:国家自然科学基金(61801065, 61771082, 61871062)，重庆市高校创新团队建设计划(CXTDX201601020)
详细信息
作者简介:王汝言：男，1969年生，教授，主要研究方向为泛在网络、多媒体信息处理等
梁颖杰：女，1994年生，硕士生，研究方向为车联网、移动边缘计算
崔亚平：男，1986年生，讲师，研究方向为毫米波通信、多天线技术、车联网等
通讯作者:梁颖杰 liangyj10111@163.com
中图分类号:TN919.2计量
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被引次数:0
出版历程
收稿日期:2019-01-25
修回日期:2019-07-16
网络出版日期:2019-09-20
刊出日期:2020-01-21
Intelligent Resource Allocation Algorithm for Multi-platform Offloading in Vehicular Networks
Ruyan WANG,Yingjie LIANG,,
Yaping CUI
1. School of Communication and Information Engineering, Chongqing University of Posts andTelecommunications, Chongqing 400065, China
2. Chongqing Key Laboratory of Optical Communication and Networks , Chongqing 400065, China
3. Chongqing Key Laboratory of Ubiquitous Sensing and Networking, Chongqing 400065, China
Funds:The National Natural Science Foundation of China (61801065, 61771082, 61871062), The Program for Innovation Team Building at Institutions of Higher Education in Chongqing (CXTDX201601020)
摘要
摘要:为了降低计算任务的时延和系统的成本，移动边缘计算(MEC)被用于车辆网络，以进一步改善车辆服务。该文在考虑计算资源的情况下对车辆网络时延问题进行研究，提出一种多平台卸载智能资源分配算法，对计算资源进行分配，以提高下一代车辆网络的性能。该算法首先使用K临近(KNN)算法对计算任务的卸载平台(云计算、移动边缘计算、本地计算)进行选择，然后在考虑非本地计算资源分配和系统复杂性的情况下，使用强化学习方法，以有效解决使用移动边缘计算的车辆网络中的资源分配问题。仿真结果表明，与任务全部卸载到本地或MEC服务器等基准算法相比，提出的多平台卸载智能资源分配算法实现了时延成本的显著降低，平均可节省系统总成本达80%。
关键词:车辆网络/
移动边缘计算/
资源分配/
强化学习
Abstract:In order to reduce the delay of computing tasks and the total cost of the system, Mobile Eedge Computing (MEC) technology is applied to vehicular networks to improve further the service quality. The delay problem of vehicular networks is studied with the consideration of computing resources. In order to improve the performance of the next generation vehicular networks, a multi-platform offloading intelligent resource allocation algorithm is proposed to allocate the computing resources. In the proposed algorithm, the K-Nearest Neighbor (KNN) algorithm is used to select the offloading platform (i.e., cloud computing, mobile edge computing, local computing) for computing tasks. For the computing resource allocation problem and system complexity in non-local computing, reinforcement learning is used to solve the optimization problem of resource allocation in vehicular networks using the mobile edge computing technology. Simulation results demonstrate that compared with the baseline algorithms (i.e., all tasks offload to the local or MEC server), the proposed multi-platform offloading intelligent resource allocation algorithm achieves a significant reduction in latency cost, and the average system cost can be saved by 80%.
Key words:Vehicular networks/
Mobile Edge Computing (MEC)/
Resource allocation/
Reinforcement learning
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