Analysis of flexible traffic control method in SDN

They enable efficient management of resources and network traffic, a definite advantage in the age of increasingly complex networks requiring dynamic management. By centralizing control and enabling flexible management, SDN offers new opportunities for network optimization. Nevertheless, fully realizing the potential of SDN requires the development of advanced and adaptive control methods. This article focuses on analyzing current methods of flexible control for SDN networks and presenting a solution to improve the efficiency and adaptability of network management. The approach presented is based on the application of machine learning, specifically the Reinforcement Learning (RL) [2]. This technique allows networks to make autonomous decisions based on previous experiences and dynamically changing conditions, which is similar to the way humans learn. The goal of the proposed solution is to not only increase network performance, but to improve its flexibility and real-time adaptability. The use of reinforcement learning enables dynamic and flexible control of network traffic, resulting in more efficient and responsive resource management [3]. The article reviews existing solutions and describes in detail the original approach developed in-its own, pointing out its potential benefits and implementation possibilities.