Hybrid Edge-Cloud Collaborator Resource Scheduling Approach Based on Deep Reinforcement Learning and Multiobjective Optimization-Reference-Cited by-同舟云学术

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Hybrid Edge-Cloud Collaborator Resource Scheduling Approach Based on Deep Reinforcement Learning and Multiobjective Optimization

Published:2024-01 Issue:1 Volume:73 Page:192-205
ISSN:0018-9340
Container-title:IEEE Transactions on Computers
language:
Short-container-title:IEEE Trans. Comput.

Author:

Zhang Jiangjiang¹^ORCID,Ning Zhenhu¹^ORCID,Waqas Muhammad²^ORCID,Alasmary Hisham³^ORCID,Tu Shanshan¹^ORCID,Chen Sheng⁴^ORCID

Affiliation:

1. Faculty of Information Technology, Beijing University of Technology, Beijing, China

2. School of Computing and Mathematical Science, Faculty of Engineering and Science, University of Greenwich, London, U.K.

3. Department of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia

4. School of Electronics and Computer Science, University of Southampton, Southampton, U.K.

Funder

National Key Research and Development Project of China

National Natural Science Foundation of China

King Khalid University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Hardware and Architecture,Theoretical Computer Science,Software

Link

http://xplorestaging.ieee.org/ielx7/12/10372122/10305543.pdf?arnumber=10305543

Reference45 articles.

1. Mobility-Aware Joint Task Scheduling and Resource Allocation for Cooperative Mobile Edge Computing

2. Resource Scheduling in Edge Computing: A Survey

3. Dynamic Offloading and Resource Scheduling for Mobile-Edge Computing With Energy Harvesting Devices

4. Slicing-Based Reliable Resource Orchestration for Secure Software-Defined Edge-Cloud Computing Systems

5. Risk-Aware Energy Scheduling for Edge Computing With Microgrid: A Multi-Agent Deep Reinforcement Learning Approach

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