Dynamic Service Caching Aided Computation Offloading Optimization Algorithm for Mobile-Edge Networks
Xie, Bo Xie, Jinhua Cui, Haixia He, Yejun Guizani, Mohsen
Xie, Bo
Xie, Jinhua
Cui, Haixia
He, Yejun
Guizani, Mohsen
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
The widespread adoption of computation- and communication-intensive applications, such as object detection, VR/AR, and telemedicine, has significantly alleviated transmission pressure on backbone networks and improved user experience. However, efficiently managing and computing these tasks on user sides remains a significant challenge, particularly under resource-constrained conditions. To address this problem, we propose a new service caching decision method based on deep dueling double Q-network (D3QN) by employing a learnable policy to handle the unknown task requests and determine the optimal caching strategies. Additionally, the limited storage capacity of edge servers (ES) is mitigated by forwarding the resource-intensive or infrequently requested tasks to the cloud data centers (CDC). The channel selection problem is modeled as a multiuser game and a distributed method is developed to achieve the Nash Equilibrium (NE). Simulation results demonstrate that the proposed method outperforms the existing benchmarks, showcasing its effectiveness in managing complex, dynamic environments.
Citation
B. Xie, J. Xie, H. Cui, Y. He and M. Guizani, "Dynamic Service Caching Aided Computation Offloading Optimization Algorithm for Mobile-Edge Networks," in IEEE Internet of Things Journal, vol. 12, no. 13, pp. 24789-24802, 1 July1, 2025, doi: 10.1109/JIOT.2025.3555978
Source
IEEE Internet of Things Journal
Conference
Keywords
Channel selection, computation offloading, deep dueling double Q-network (D3QN), mobile cloud-edge cooperative computing, service caching
Subjects
Source
Publisher
IEEE