$\mu$-DDRL: A QoS-Aware Distributed Deep Reinforcement Learning Technique for Service Offloading in Fog computing Environments-Reference-Cited by-同舟云学术

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$\mu$-DDRL: A QoS-Aware Distributed Deep Reinforcement Learning Technique for Service Offloading in Fog computing Environments

Published:2023 Issue: Volume: Page:1-13
ISSN:1939-1374
Container-title:IEEE Transactions on Services Computing
language:
Short-container-title:IEEE Trans. Serv. Comput.

Author:

Goudarzi Mohammad¹^ORCID,Rodriguez Maria A.²,Sarvi Majid³^ORCID,Buyya Rajkumar¹^ORCID

Affiliation:

1. Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Australia

2. School of Computing and Information Systems, University of Melbourne, Australia

3. Department of Infrastructure Engineering, University of Melbourne, Australia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Information Systems and Management,Computer Networks and Communications,Computer Science Applications,Hardware and Architecture

Link

http://xplorestaging.ieee.org/ielx7/4629386/4629387/10316602.pdf?arnumber=10316602

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. Dynamic service provisioning in heterogeneous fog computing architecture using deep reinforcement learning;The Journal of Supercomputing;2024-07-29

2. TinyML: Tools, applications, challenges, and future research directions;Multimedia Tools and Applications;2023-09-09

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