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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.

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.

Corresponding Author

Chen Zheyi

DOI

10.16182/j.issn1004731x.joss.24-0574

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