Q-Learning Based Adaptive Flow Control
Author:
Affiliation:
1. School of Software, Northwestern Polytechnical University,Xi'an,China
2. School of Computer Science, Northwestern Polytechnical University,Xi'an,China
3. China Unicom Western Innovation Research Institute,R&D Department 1,Xi'an,China
Funder
Key Research and Development Plan of Shaanxi Province
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10294323/10294356/10294952.pdf?arnumber=10294952
Reference15 articles.
1. Network Traffic Prediction Method Based on Time Series Characteristics;huo;Proceedings of the 10th International Conference on Computer Engineering and Networks(CENet2020),2020
2. Resource Scheduling Algorithms for Burst Network Flow in Edge Computing;yan;Proceedings of the 11th International Conference on Computer Engineering and Networks (CENet2021)Part II(pp 723-732),2021
3. Abnormal Flow Monitoring of Industrial Control Network Based on Neural Network
4. 5G network traffic control: a temporal analysis and forecasting of cumulative network activity using machine learning and deep learning technologies
5. A Flow Control Method Based on Deep Deterministic Policy Gradient;mao;Proceedings of the 10th International Conference on Computer Engineering and Networks(CENet2020),2020
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