SDN和MEC架构下V2X卸载与资源分配

张海波; 王子心; 贺晓帆

doi:10.11959/j.issn.1000-436x.2020023

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SDN和MEC架构下V2X卸载与资源分配

学术论文 | 更新时间：2024-06-05

- SDN和MEC架构下V2X卸载与资源分配
- V2X offloading and resource allocation under SDN and MEC architecture
- 通信学报 2020年41卷第1期页码：114-124
- 作者机构：
  
  1. 重庆邮电大学通信与信息工程学院，重庆 400065
  2. 武汉大学电子信息学院，湖北武汉 430072
- 作者简介：
  
  [ "张海波（1979- ），男，重庆人，博士，重庆邮电大学副教授、硕士生导师，主要研究方向为车联网、移动边缘计算等" ]
  [ "王子心（1995- ），女，重庆人，重庆邮电大学硕士生，主要研究方向为车联网、移动边缘计算" ]
  [ "贺晓帆（1985- ），男，河北保定人，博士，武汉大学教授，主要研究方向为无线资源优化" ]
- 基金信息：
  
  国家自然科学基金资助项目(61801065);国家自然科学基金资助项目(61601071);长江学者和创新团队发展计划基金资助项目(IRT16R72);重庆市基础与前沿基金资助项目(cstc2018jcyjAX0463)
- DOI：10.11959/j.issn.1000-436x.2020023
  中图分类号： TN929.5
- 网络出版日期：2020-01，
  
  纸质出版日期：2020-01-25
- 稿件说明：
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张海波, 王子心, 贺晓帆. SDN和MEC架构下V2X卸载与资源分配[J]. 通信学报, 2020,41(1):114-124.

Haibo ZHANG, Zixin WANG, Xiaofan HE. V2X offloading and resource allocation under SDN and MEC architecture[J]. Journal on communications, 2020, 41(1): 114-124.
张海波, 王子心, 贺晓帆. SDN和MEC架构下V2X卸载与资源分配[J]. 通信学报, 2020,41(1):114-124. DOI： 10.11959/j.issn.1000-436x.2020023.

Haibo ZHANG, Zixin WANG, Xiaofan HE. V2X offloading and resource allocation under SDN and MEC architecture[J]. Journal on communications, 2020, 41(1): 114-124. DOI： 10.11959/j.issn.1000-436x.2020023.

摘要

针对车到万物（V2X）场景下复杂的网络状态与海量的计算数据为车载网络带来的时延能耗增加和服务质量下降的严峻问题，构建了移动边缘计算（MEC）和软件定义网络（SDN）相结合的车载网络框架。MEC 将云服务下沉至无线网络边缘从而弥补了远程云计算所带来时延抖动，SDN控制器可从全局角度感知网络信息，灵活地调度资源，控制卸载流量。为了进一步降低系统开销，提出一种联合任务卸载与资源分配机制，对基于MEC的V2X卸载与资源分配进行建模，给出了最优卸载决策、通信和计算资源分配方案。考虑到问题的NP-hard属性，利用Agglomerative Clustering匹配初始卸载节点，并采用Q-learning进行资源分配；将卸载决策建模为完全势博弈，通过势函数构造证明纳什均衡。仿真结果表明，相比于其他机制，该机制能有效降低系统开销。

Abstract

To address the serious problem of delay and energy consumption increase and service quality degradation caused by complex network status and huge amounts of computing data in the scenario of vehicle-to-everything (V2X)

a vehicular network architecture combining mobile edge computing (MEC) and software defined network (SDN) was constructed.MEC sinks cloud serviced to the edge of the wireless network to compensate for the delay fluctuation caused by remote cloud computing.The SDN controller could sense network information from a global perspective

flexibly schedule resources

and control offload traffic.To further reduce the system overhead

a joint task offloading and resource allocation scheme was proposed.By modeling the MEC-based V2X offloading and resource allocation

the optimal offloading decision

communication and computing resource allocation scheme were derived.Considering the NP-hard attribute of the problem

Agglomerative Clustering was used to select the initial offloading node

and Q-learning was used for resource allocation.The offloading decision was modeled as an exact potential game

and the existence of Nash equilibrium was proved by the potential function structure.The simulation results show that

as compared to other mechanisms

the proposed mechanism can effectively reduce the system overhead.

关键词

Keywords

references

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