DATE: Disturbance-Aware Traffic Engineering with Reinforcement Learning in Software-Defined Networks
Author:
Funder
National Natural Science Foundation of China
Beijing Institute of Technology
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9521186/9521256/09521343.pdf?arnumber=9521343
Cited by 16 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. TITE: A transformer-based deep reinforcement learning approach for traffic engineering in hybrid SDN with dynamic traffic;Future Generation Computer Systems;2024-12
2. GameTE: A Game-Theoretic Distributed Traffic Engineering in Trustless Multi-Domain SDN;2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS);2024-07-23
3. DQS: A QoS-driven routing optimization approach in SDN using deep reinforcement learning;Journal of Parallel and Distributed Computing;2024-06
4. Enabling efficient routing for traffic engineering in SDN with Deep Reinforcement Learning;Computer Networks;2024-03
5. Routing Optimization With Deep Reinforcement Learning in Knowledge Defined Networking;IEEE Transactions on Mobile Computing;2024-02
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