A DRL-Driven Intelligent Optimization Strategy for Resource Allocation in Cloud-Edge-End Cooperation Environments

A DRL-Driven Intelligent Optimization Strategy for Resource Allocation in Cloud-Edge-End Cooperation Environments

Complex dynamic services and heterogeneous network environments make the asymmetrical control a curial issue to handle on the Internet. With the advent of the Internet of Things (IoT) and the fifth generation (5G), the emerging network applications lead to the explosive growth of mobile traffic whil...

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Bibliographic Details
Main Authors: Chao Fang, Tianyi Zhang, Jingjing Huang, Hang Xu, Zhaoming Hu, Yihui Yang, Zhuwei Wang, Zequan Zhou, Xiling Luo
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Symmetry
Subjects:
resource allocation
cloud-edge-end cooperation networks
deep reinforcement learning
in-network caching
request aggregation
Online Access:https://www.mdpi.com/2073-8994/14/10/2120

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https://www.mdpi.com/2073-8994/14/10/2120

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