An Architecture to Enable Machine-Learning-Based Task Migration for Multi-Core Real-Time Systems
Author:
Affiliation:
1. fortiss GmbH, Research Institute of the Free State of Bavaria,Munich,Germany
2. Technical University of Munich,Department of Informatics,Munich,Germany
Funder
European Union (EU)
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9691770/9691932/09691957.pdf?arnumber=9691957
Reference20 articles.
1. Task-set generator for schedulability analysis using the TACLebench benchmark suite
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3. KIA4SM - Cooperative integration architecture for future smart mobility solutions;eckl;CoFAT Conference on Future Automotive Technology,2015
Cited by 1 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. A Generalistic Approach to Machine-Learning-Supported Task Migration on Real-Time Systems;Journal of Low Power Electronics and Applications;2022-05-03
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