Journal of System Simulation
Abstract
Abstract: In mobile edge computing (MEC), to satisfy diverse user demands by jointly optimizing service caching and computation offloading and address low-efficiency resource utilization caused by irrational resource allocation, this paper proposed a novel joint optimization of service caching and computation offloading with a convex-optimization-enabled deep reinforcement learning (JCO-CR) method. Additionally, a new model for digital twin cloud-edge networks (DTCEN) was constructed. The joint optimization of service caching and computation offloading was decoupled into two sub-problems, which were solved by an improved deep reinforcement learning method and convex optimization theory, respectively. Simulation experiments demonstrate that the proposed JCO-CR method can reduce long-term service latency and achieve better performance under different scenarios.
Recommended Citation
Zheng, Jiayu; Mai, Zhuxue; and Chen, Zheyi
(2025)
"Optimization of Service Caching and Computation Offloading in Digital Twin Cloud-edge Networks,"
Journal of System Simulation: Vol. 37:
Iss.
11, Article 4.
DOI: 10.16182/j.issn1004731x.joss.24-0574
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss11/4
First Page
2741
Last Page
2753
CLC
TP393
Recommended Citation
Zheng Jiayu, Mai Zhuxue, Chen Zheyi. Optimization of Service Caching and Computation Offloading in Digital Twin Cloud-edge Networks[J]. Journal of System Simulation, 2025, 37(11): 2741-2753.
DOI
10.16182/j.issn1004731x.joss.24-0574
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