Joint Task and Computing Resource Allocation in Distributed Edge Computing Systems via Multi-Agent Deep Reinforcement Learning-Reference-Cited by-同舟云学术

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Joint Task and Computing Resource Allocation in Distributed Edge Computing Systems via Multi-Agent Deep Reinforcement Learning

Published:2024-07 Issue:4 Volume:11 Page:3479-3494
ISSN:2327-4697
Container-title:IEEE Transactions on Network Science and Engineering
language:
Short-container-title:IEEE Trans. Netw. Sci. Eng.

Author:

Chen Yan¹^ORCID,Sun Yanjing²^ORCID,Yu Hao³^ORCID,Taleb Tarik⁴^ORCID

Affiliation:

1. Zhejiang Lab, Hangzhou, China

2. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China

3. Center of Wireless Communications, University of Oulu, Oulu, Finland

4. Ruhr University Bochum, Bochum, Germany

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488902/10554364/10465255.pdf?arnumber=10465255

Reference52 articles.

1. Edge Computing: Vision and Challenges

2. A Survey on Mobile Edge Computing: The Communication Perspective

3. Service-aware 6G: An intelligent and open network based on the convergence of communication, computing and caching

4. Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

5. Edge-Cloud Collaboration Enabled Video Service Enhancement: A Hybrid Human-Artificial Intelligence Scheme

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Utility-Driven End-to-End Network Slicing for Diverse IoT Users in MEC: A Multi-Agent Deep Reinforcement Learning Approach;Sensors;2024-08-28

2. Dynamic Edge AI Service Management and Adaptation Via Off-Policy Meta-Reinforcement Learning and Digital Twin;ICC 2024 - IEEE International Conference on Communications;2024-06-09

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