Journal of System Simulation
Abstract
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.
Recommended Citation
Huang, Sijin; Wen, Jia; and Chen, Zheyi
(2025)
"Intelligent Service Migration towards MEC-based IoV Systems,"
Journal of System Simulation: Vol. 37:
Iss.
2, Article 6.
DOI: 10.16182/j.issn1004731x.joss.23-1142
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss2/6
First Page
379
Last Page
391
CLC
TP393
Recommended Citation
Huang Sijin, Wen Jia, Chen Zheyi. Intelligent Service Migration towards MEC-based IoV Systems[J]. Journal of System Simulation, 2025, 37(2): 379-391.
DOI
10.16182/j.issn1004731x.joss.23-1142
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