LOAD BALANCING SYSTEM AND QUALITY OF SERVICE OPTIMIZATION

Currently the growing demand for connectivity coupled with the exponential increase in data traffic presents significant challenges for WiFi networks including the management of quality of service and load balancing. These problems are aggravated by technological evolution and the integration of systems such as Artificial Intelligence and IoT which generate volumes of data such that current infrastructures do not handle efficiently; this project proposed to design a system based on SDN environment that using the Ryu controller integrated QoS and load balancing algorithms in SDN switches supported by machine learning techniques. The methodology applied included analysis design implementation evaluation and optimization through simulations and tests in different controlled scenarios; among the expected results the system improved aspects such as traffic distribution latency reduction packet loss and other network resources ensuring a better user experience and greater efficiency in the operation of networks in the face of growing demands.