QoS management using neural networks

With the constant growth of the number of Internet users, new requirements arise for the bandwidth of communication networks. To effectively manage the data transmission network, it is necessary to apply routing, traffic management and congestion control methods. In this article, we will look at Quality of Service management (QoS) and the problems associated with congestion in the network. QoS is a technology that includes a set of mechanisms and algorithms for processing various types of traffic, taking into account their priority on network devices. QoS tools allow efficient use of available network resources in conditions of their limitations in modern networks and meet the increased requirements of services and users for network services. In this article, we will also consider the use of neural networks in QoS management. Neural networks represent a great potential for optimizing the operation of networks and improving the quality of service. They can be used to predict and adapt traffic, determine optimal routes and load balancing. However, further research is required to fully exploit the potential of neural networks in QoS management. It is necessary to optimize neural network models and develop specialized learning algorithms in order to maximize the possibilities of this approach in the future. The use of neural networks in QoS management can significantly improve network performance and provide more efficient traffic management. However, in order to achieve these goals, it is necessary to pay more attention to the research and development of this area.