Service Caching and Task Migration Mechanism Based on Internet of Vehicles

ZUO Linli; XIA Shichao; LI Yun; PAN Junnan; CHEN Bingyi

doi:10.11999/JEIT241097

Volume 47 Issue 8

Aug. 2025

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ZUO Linli, XIA Shichao, LI Yun, PAN Junnan, CHEN Bingyi. Service Caching and Task Migration Mechanism Based on Internet of Vehicles[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2563-2572. doi: 10.11999/JEIT241097

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ZUO Linli, XIA Shichao, LI Yun, PAN Junnan, CHEN Bingyi. Service Caching and Task Migration Mechanism Based on Internet of Vehicles[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2563-2572. doi: 10.11999/JEIT241097

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Service Caching and Task Migration Mechanism Based on Internet of Vehicles

doi: 10.11999/JEIT241097 cstr: 32379.14.JEIT241097

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)

Received Date: 2024-12-12
Rev Recd Date: 2025-07-09

Available Online: 2025-07-25

Publish Date: 2025-08-27

Abstract

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

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References(20)

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