车联网的服务缓存和任务迁移机制

左琳立; 夏士超; 李云; 潘俊男; 陈冰旖

doi:10.11999/JEIT241097

车联网的服务缓存和任务迁移机制

doi: 10.11999/JEIT241097 cstr: 32379.14.JEIT241097

左琳立^{1, 2},
夏士超^3, ,,
李云¹,
潘俊男¹,
陈冰旖¹

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
重庆电子科技职业大学通信工程学院重庆 401331
3.
重庆邮电大学计算机科学与技术学院重庆 400065

基金项目: 国家自然科学基金(62301099, 62071077)，重庆市自然科学基金(CSTB2024NSCQ-QCXMX0063, cstc2024ycjh-bgzxm003)，重庆市教育委员会科学技术项目(KJQN202203122, KJQN202300638)

详细信息

作者简介:
左琳立：男，博士生，讲师，研究方向为无线通信技术、边缘智能等

夏士超：男，博士，硕士生导师，讲师，研究方向为无线网络通信、通感算一体化技术、边缘计算等

李云：男，博士，博士生导师，教授，研究方向为云计算、边缘智能、无线网络资源管理

潘俊男：男，博士生，研究方向为无人机通信、人工智能等

陈冰旖：女，硕士生，研究方向为边缘计算、车联网

通讯作者:
夏士超　xiashichao@cqupt.edu.cn

中图分类号: TN929.5
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出版历程
- 收稿日期: 2024-12-12
- 修回日期: 2025-07-09
- 网络出版日期: 2025-07-25
- 刊出日期: 2025-08-27

Service Caching and Task Migration Mechanism Based on Internet of Vehicles

ZUO Linli^{1, 2},
XIA Shichao^{3
, ,},
LI Yun¹,
PAN Junnan¹,
CHEN Bingyi¹

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
School of Communication Engineering, Chongqing Polytechnic University of Electronic Technology, Chongqing 401331, China
3.
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62301099, 62071077), The Natural Science Foundation of Chongqing, China (CSTB2024NSCQ-QCXMX0063, cstc2024ycjh-bgzxm003), The Scientific and Technological Research Project of Chongqing Education Commission (KJQN202203122, KJQN202300638)

摘要

摘要: 近年来，随着车联网(IoV)应用的迅猛增长，为满足其对低时延和高效率计算服务需求，并缓解回程链路的传输压力，移动边缘计算(MEC)技术被广泛应用于车联网领域。然而，车辆高移动性使得边缘服务缓存和任务迁移的实现具有很强的挑战性。为此，针对车联网动态环境的特点，该文提出一种适应车联网动态环境特性的服务缓存和任务迁移联合优化算法(SCTMA)，基于多智能体深度确定性策略梯度方法，在考虑车辆用户与路边单元(RSU)及基站之间交互不确定性的前提下，对边缘服务缓存和任务迁移进行联合优化。仿真结果表明，所提算法能降低缓存和任务迁移成本，提高缓存命中率。
- 车联网 /
- 服务缓存 /
- 任务迁移 /
- 多智能体深度强化学习
Abstract: Objective In the era of digital transformation and smart mobility, the Internet of Vehicles (IoV) has emerged as a transformative paradigm reshaping transportation systems and vehicle-related services. In recent years, the proliferation of IoV applications has led to the generation and processing of large volumes of real-time data, requiring ultra-low latency and high-efficiency computation to maintain seamless functionality and ensure high-quality user experiences. To meet these demands, Mobile Edge Computing (MEC) has been widely adopted in the IoV domain, effectively reducing the load on backhaul links. However, the dynamic and mobile nature of vehicular networks poses significant challenges to the effective deployment of edge services and the efficient management of task migration. Vehicles continuously move across regions with heterogeneous network conditions, edge node coverage, and service availability. Conventional static or rule-based approaches for service caching and task migration often fail to adapt to these environmental dynamics, leading to degraded performance, frequent service interruptions, and elevated energy consumption. This study proposes a Joint Service Caching and Task Migration Algorithm (SCTMA) tailored to the dynamic characteristics of the IoV environment. By incorporating machine learning, optimization techniques, and context-aware decision-making, SCTMA dynamically adjusts caching and migration strategies to ensure that appropriate services are delivered to suitable edge nodes at the optimal time, thereby minimizing latency and improving resource utilization. Methods This study systematically considers multiple constraints within the IoV system, including caching decisions, the number of cached services, cache capacity, CPU resource consumption, and task migration policies at edge nodes. To jointly optimize service caching and task migration under these constraints, a Markov Decision Process (MDP) model is constructed. The MDP framework captures the temporal dynamics of the IoV environment, wherein system states, such as vehicle location, service demand, and cache status evolve over time. The reward function is formulated to balance competing objectives, including minimizing latency, reducing energy consumption, and improving the cache hit ratio. To address inefficient utilization of Base Station (BS) caching resources and mitigate storage waste, the concept of a service hit ratio is introduced. Based on this ratio, BSs proactively cache frequently requested services, thereby reducing latency and energy usage during Vehicle User (VU) service requests and enhancing overall caching efficiency. A task migration algorithm is also developed, incorporating vehicle velocity to estimate the remaining dwell time of a VU within the coverage area of a RoadSide Unit (RSU). This estimation is used to compute the associated service data migration volume and assess migration costs. Building on this framework, a Joint SCTMA is proposed. SCTMA employs the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method to address uncertainties in multi-agent settings. This approach reduces system communication and computation costs, optimizes migration notification strategies, and improves the cache hit ratio. Results and Discussions Simulation results indicate that the proposed SCTMA algorithm effectively reduces caching and task migration costs while improving the cache hit ratio. Following training, the system’s long-term average reward under SCTMA markedly exceeds that of baseline algorithms (Fig. 3). Specifically, SCTMA maintains the long-term average reward at approximately –30, whereas the best-performing comparative method stabilizes at around –38, corresponding to an improvement of at least 21.05%. Further analysis of edge device caching performance (Fig. 5(a), Fig. 5(b)) shows that as the maximum cache capacity increases, the system using SCTMA consistently achieves the highest cache hit ratio across all tested scenarios. Conclusions In edge computing-enabled IoV ecosystems, where vehicles interact with infrastructure and peer nodes through interconnected edge networks, this study examines decision-making mechanisms for service hit rate optimization and task migration. By formulating the joint optimization of service caching and task migration as A MDP, a Joint SCTMA is proposed. Simulation results show that SCTMA reduces service caching and task migration costs, shortens service request latency for VU, and improves overall system performance. However, the current study assumes an idealized IoV environment. Future research should evaluate the algorithm’s robustness and efficiency under real-world conditions.
- Internet of Vehicles (IoV) /
- Service caching /
- Task migration /
- Multi-agent deep reinforcement learning

HTML全文

图 1 IoV网络模型

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图 2 SCTMA算法架构

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图 3 不同算法训练收敛情况

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图 4 不同车辆数目下的性能比较

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图 5 不同最大缓存容量下的性能比较

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图 6 不同车辆行驶速度下的性能比较

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表 1 仿真参数

参数	值
BS数量	3
MEC服务器数量	3
RSU数量	10
车辆数量	15
服务类型数量	10
服务大小	100～600 MB
车辆速度	30 m/s
RSU覆盖半径	150 m
BS覆盖半径	500 m
MEC服务器最大缓存容量	2 GB
MEC服务器的CPU频率	$5 \times {10^6}$ cycle/s

下载: 导出CSV

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