SDN和MEC架构下V2X卸载与资源分配

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

摘要/Abstract

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。

关键词: 车联网, 移动边缘计算, 软件定义网络, 资源分配

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.

Key words: vehicular network, mobile edge computing, software defined network, resource allocation

中图分类号:

TN929.5

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.

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参数	数值
任务计算大小d_i /KB	400～500
计算任务所需CPU周期s_i /Megacycle	1 200～2 000
上行传输信道数量K	10
单小区车辆数目	18
信道总带宽/MHz	20
高斯白噪声功率N₀ /dBm	-60
权重因子设置α=β	0.5
车辆计算能力F_i/GHz	0.7 ～1
MEC服务器计算能力 f_j /GHz	4
车辆最大发射功率 p_max/dBm	20
RSU的覆盖半径/m	250