DRL-TA: A Type-aware Task Scheduling and Load Balancing Method based on Deep Reinforcement Learning in Heterogeneous Computing Environment-Reference-Cited by-同舟云学术

DRL-TA: A Type-aware Task Scheduling and Load Balancing Method based on Deep Reinforcement Learning in Heterogeneous Computing Environment

Published:2022-10 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)
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Author:

Sun Changyong¹,Yang Tan¹,Lei Youxun¹

Affiliation:

1. School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications,State Key Laboratory of Networking and Switching Technology,Beijing,China

Funder

National Key Research and Development Program of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10097829/10097885/10098032.pdf?arnumber=10098032

Reference31 articles.

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4. Q-learning applied to the problem of scheduling on heterogeneous architectures;hajoui;International Journal of Computer Science and Network Security,2018

5. Improving the cost efficiency of large-scale cloud systems running hybrid workloads - A case study of Alibaba cluster traces

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