Analysis of flexible traffic control method in SDN

TL;DR

This paper analyzes flexible traffic control in SDN networks and proposes a reinforcement learning-based solution to enhance network performance, adaptability, and real-time control in dynamic environments.

Contribution

It introduces a novel reinforcement learning approach for autonomous, flexible traffic management in SDN, improving upon existing methods.

Findings

Enhanced network adaptability and performance.

Reinforcement learning enables autonomous decision-making.

Improved real-time traffic control capabilities.

Abstract

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.