Design and development of an intent-based intelligent network using machine learning for QoS provisioning

The demand for bandwidth is currently a challenge for the use of the Internet that companies require, it is common to see daily the exorbitant amount of data that circulate through the network as files, calls, video calls, online shopping or subscription streaming services, this generates a bottleneck in network traffic therefore this makes maintenance and management difficult requiring time and human effort. To solve this problem it is proposed to use the architecture of differentiated services to prioritize a type of traffic and obtain Quality of Service (QoS), in addition the automation of activities will be used, that is, with artificial intelligence (AI) a neural network is developed to identify patterns and obtain predictions, machine learning (ML) that will predict when there will be events that alter the resources in the network. To demonstrate the effectiveness of the method, a proprietary dataset generated with data from the developed infrastructure is used, therefore the methods are evaluated with the quality metric Mean Absolute Error (MAE). At the end an Intention Based Network (IBN) will have been implemented, therefore this research intends to leave a base so that the proposed system can be improved or other methods can be developed to automate data centers.