Joint task offloading and resource allocation for multi-user and multi-server MEC networks: A deep reinforcement learning approach with multi-branch architecture-Reference-Cited by-同舟云学术

Joint task offloading and resource allocation for multi-user and multi-server MEC networks: A deep reinforcement learning approach with multi-branch architecture

Published:2023-11 Issue: Volume:126 Page:106790
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Sun Yu,He Qijie^ORCID

Funder

National Natural Science Foundation of China

Science and Technology Major Project of Guangxi

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference38 articles.

1. Mobile edge computing: A survey;Abbas;IEEE Internet Things J.,2017

2. Energy-efficient resource allocation for mobile edge computing-based augmented reality applications;Al-Shuwaili;IEEE Wirel. Commun. Lett.,2017

3. Energy-optimized partial computation offloading in mobile-edge computing with genetic simulated-annealing-based particle swarm optimization;Bi;IEEE Internet Things J.,2020

4. Non-cooperative game algorithms for computation offloading in mobile edge computing environments;Chen;J. Parallel Distrib. Comput.,2023

5. Chen, Z., Hu, W., Wang, J., Zhao, S., Amos, B., Wu, G., Ha, K., Elgazzar, K., Pillai, P., Klatzky, R., et al., 2017a. An empirical study of latency in an emerging class of edge computing applications for wearable cognitive assistance. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing. pp. 1–14.

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