张海波¹,
程妍^1,,,
刘开健¹,
贺晓帆²
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 武汉 430000
基金项目:国家自然科学基金(61801065, 61601071)，****和创新团队发展计划基金项目(IRT16R72)，重庆市基础与前沿项目(cstc2018jcyjAX0463)

详细信息

作者简介:张海波：男，1979年生，副教授，研究方向为无线资源管理
程妍：女，1994年生，硕士生，研究方向为移动边缘计算
刘开健：女，1981年生，讲师，研究方向为最优化算法
贺晓帆：男，1985年生，助理教授，研究方向为无线资源优化

通讯作者:程妍　2311837009@qq.com

中图分类号:TN929.5

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出版历程

收稿日期:2019-07-29
修回日期:2020-02-21
网络出版日期:2020-03-20
刊出日期:2020-06-22

The Mobility Management Strategies by Integrating Mobile Edge Computing and CDN in Vehicular Networks

Haibo ZHANG¹,
Yan CHENG^1,,,
Kaijian LIU¹,
Xiaofan HE²
1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2. School of Electronic Information, Wuhan University, Wuhan 430000, China
Funds:The National Natural Science Foundation of China (61801065, 61601071), The Program for Changjiang Scholars and Innovative Research Team in University (IRT16R72), The General Project on Foundation and Cutting-edge Research Plan of Chongqing (cstc2018jcyjAX0463)


摘要
摘要:由于车载应用的普及和车辆数量的增加，路边基础设施的物理资源有限，当大量车辆接入车联网时能耗与时延同时增加，通过整合内容分发网络(CDN)和移动边缘计算(MEC)的框架可以降低时延与能耗。在车联网中，车辆移动性对云服务的连续性提出了重大挑战。因此，该文提出了移动性管理(MM)来处理该问题。采用开销选择的动态信道分配(ODCA)算法避免乒乓效应且减少车辆在小区间的切换时间。采用基于路边单元(RSU)调度的合作博弈算法进行虚拟机迁移并开发基于学习的价格控制机制，以有效地处理MEC的计算资源。仿真结果表明，所提算法相比于现有的算法能够提高资源利用率且减少开销。
关键词:车联网/
移动边缘计算/
内容分发网络/
小区间的切换/
虚拟机迁移
Abstract:Due to the popularity of vehicle applications and the increase of the number of vehicles, the physical resources of roadside infrastructure are limited. When a large number of vehicles are connected to the vehicle networks, the energy consumption and latency are simultaneously increased. The framework for integrating the Content Delivery Network (CDN) and Mobile Edge Computing (MEC) can reduce the latency and energy consumption. In vehicle network, vehicle mobility poses a major challenge to the continuity of cloud services. Therefore, Mobility Management (MM) is proposed to deal with this problem. The Dynamic Channel Allocation algorithm with Overhead selection (ODCA) is used to avoid the ping-pong effect and reduces the handover time of vehicles between cells. The cooperative game algorithm based on RoadSide Unit (RSU) is used for virtual machine migration and a learning-based price control mechanism is developed to process vehicular computation resources efficiently. The simulation results show that the proposed algorithm can improve resource utilization and reduce overhead compared with the existing algorithms.
Key words:Vehicular networks/
Mobile Edge Computing (MEC)/
Content Delivery Network (CDN)/
Cell handover/
Virtual machine migration



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