面向边缘车联网系统的智能服务迁移方法

doi:10.16182/j.issn1004731x.joss.23-1142

系统仿真学报 ›› 2025, Vol. 37 ›› Issue (2): 379-391.doi: 10.16182/j.issn1004731x.joss.23-1142

• 研究论文 • 上一篇

面向边缘车联网系统的智能服务迁移方法

黄思进¹^,²^,³, 文佳¹^,²^,³, 陈哲毅¹^,²^,³

^1.福州大学计算机与大数据学院，福建福州 350116
^2.空间数据挖掘与信息共享教育部重点实验室，福建福州 350002
^3.福建省网络计算与智能信息处理重点实验室(福州大学)，福建福州 350116

收稿日期:2023-09-14 修回日期:2023-11-08 出版日期:2025-02-14 发布日期:2025-02-10
通讯作者: 陈哲毅
第一作者简介:黄思进(2000-)，男，硕士生，研究方向为移动边缘计算、计算卸载、服务迁移。
基金资助:
国家自然科学基金(62202103);中央引导地方科技发展资金(2022L3004);福建省财政厅科研专项经费(83021094);福建省科技经济融合服务平台(2023XRH001);福厦泉国家自主创新示范区协同创新平台(2022FX5)

Intelligent Service Migration towards MEC-based IoV Systems

Huang Sijin¹^,²^,³, Wen Jia¹^,²^,³, Chen Zheyi¹^,²^,³

^1.College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China
^2.Key Laboratory of Spatial Data Mining & Information Sharing, Ministry of Education, Fuzhou 350002, China
^3.Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou 350116, China

Received:2023-09-14 Revised:2023-11-08 Online:2025-02-14 Published:2025-02-10
Contact: Chen Zheyi

摘要/Abstract

摘要：

针对车辆移动过程中服务质量(QoS)下降的问题，提出了一种基于凸优化使能深度强化学习的服务迁移(service migration via convex-optimization-enabled deep reinforcement learning，SeMiR)方法。将优化问题分解为两个子问题并分别求解；针对服务迁移子问题，设计了一种基于改进深度强化学习的服务迁移方法，以探索最优迁移策略；针对资源分配子问题，设计了一种基于凸优化的资源分配方法，以推导给定迁移决策下每台MEC服务器的最优资源分配，提升服务迁移的性能。实验结果表明：与基准方法相比，SeMiR方法能够有效提升车辆的QoS，在各种场景下均展现出更加优越的性能。

关键词: 移动边缘计算, 车联网, 服务迁移, 资源分配, 深度强化学习, 凸优化

Abstract:

To address the problem of QoS degradation during the vehicle movement, a novel service migration via convex-optimization-enabled deep reinforcement learning (SeMiR) method is proposed. The optimization problem is decomposed into two sub-problems and solved separately. For the service migration sub-problem, an improved deep reinforcement learning based service migration method is designed to explore the optimal migration policy. For the resource allocation sub-problem, a convex optimization based resource allocation method is developed to derive the optimal resource allocation for each MEC server under the given migration decisions, thereby improving the performance of service migration. Experimental results show that the SeMiR method can achieve better QoS and superior service migration performance than benchmark methods under various scenarios.

Key words: MEC, Internet-of-Vehicles, service migration, resource allocation, DRL, convex optimization

中图分类号:

TP393

黄思进,文佳,陈哲毅 . 面向边缘车联网系统的智能服务迁移方法[J]. 系统仿真学报, 2025, 37(2): 379-391.

Huang Sijin,Wen Jia,Chen Zheyi . Intelligent Service Migration towards MEC-based IoV Systems[J]. Journal of System Simulation, 2025, 37(2): 379-391.

图/表 10

图1

图2

表1

仿真参数设置

仿真参数	设置
车辆 $u$ 的发射功率 $P u$ /W	[0.4, 0.6]
任务数据量 $D t (u)$ /MB	[0.5, 1.5]^[25]
任务处理密度 $C t (u)$ /(cycles/bit)	[200, 1 000]^{[24, 26]}
服务实例数据量 $S t (u)$ /MB	[0.5, 50]^{[14, 24]}
回程链路带宽 $χ$ /Mbps	500^[24]
单位传输延迟系数 $μ h$ /s	0.2
单位迁移延迟系数 $μ m$ /s	1
高斯噪声功率 $σ 2$ /W	10^-13[26]
参考信道增益 $α$	10^-5[26]

表1

图3

图4

图5

图6

表2

图7

图8

参考文献 26

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方法	车辆数目
方法	15	20	25	30	35
SeMiR	1.715 6	2.140 6	2.701 4	3.185 1	3.646 7
DDPG	1.798 3	2.226 5	2.795 6	3.307 8	3.770 9
DQN	1.820 1	2.336 3	2.853 3	3.344 8	3.945 9
GA	1.878 7	2.377 3	3.158 4	3.662 4	4.179 8
NM	1.858 2	2.554 1	3.343 1	4.004 9	4.522 9
AM	2.029 3	2.595 4	3.340 6	3.931 9	4.608 7
PM	2.210 7	2.771 3	3.616 5	4.272 6	4.908 6

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面向边缘车联网系统的智能服务迁移方法

Intelligent Service Migration towards MEC-based IoV Systems

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