Service Management and Energy Scheduling Toward Low-Carbon Edge Computing-Reference-Cited by-同舟云学术

Service Management and Energy Scheduling Toward Low-Carbon Edge Computing

Published:2023-01-01 Issue:1 Volume:8 Page:109-119
ISSN:2377-3782
Container-title:IEEE Transactions on Sustainable Computing
language:
Short-container-title:IEEE Trans. Sustain. Comput.

Author:

Gu Lin¹^ORCID,Zhang Weiying¹,Wang Zhongkui¹,Zeng Deze²^ORCID,Jin Hai¹^ORCID

Affiliation:

1. National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China

2. Hubei Key Laboratory of Intelligent Geo-Information Processing, School of Computer Science, China University of Geosciences, Wuhan, Hubei, China

Funder

National Natural Science Foundation of China

Open Research Projects of Zhejiang Lab

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Control and Optimization,Hardware and Architecture,Renewable Energy, Sustainability and the Environment,Software

Link

http://xplorestaging.ieee.org/ielx7/7274860/10063154/09905948.pdf?arnumber=9905948

Reference36 articles.

1. Deep Reinforcement Learning for Mobile 5G and Beyond: Fundamentals, Applications, and Challenges

2. Deep Reinforcement Learning for IoT Network Dynamic Clustering in Edge Computing

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4. DRL-cloud: Deep reinforcement learning-based resource provisioning and task scheduling for cloud service providers

5. Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

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