Analysis of flexible traffic control method in SDN
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Abstract (arXiv)
The aim of this paper is to analyze methods of flexible control in SDN networks and to propose a self-developed solution that will enable intelligent adaptation of SDN controller performance. This work aims not only to review existing solutions, but also to develop an approach that will increase the efficiency and adaptability of network management. The project uses a modern type of machine learning, Reinforcement Learning, which allows autonomous decisions of a network that learns based on its choices in a dynamically changing environment, which is most similar to the way humans learn. The solution aims not only to improve the network's performance, but also its flexibility and real-time adaptability - flexible traffic control.
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- 2024Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach
- 2024A Deep Reinforcement Learning Approach for Adaptive Traffic Routing in Next-gen Networks
- 2024Safe Load Balancing in Software-Defined-Networking
- 2026ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning
- 2025Unicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable Network-Wide Traffic Signal Control
- 2026Multi-Agent LLM Governance for Safe Two-Timescale Reinforcement Learning in SDN-IoT Defense
- 2024On Joint Convergence of Traffic State and Weight Vector in Learning-Based Dynamic Routing with Value Function Approximation
- 2025Robustness of Reinforcement Learning-Based Traffic Signal Control under Incidents: A Comparative Study