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Traffic control in data networks has recently become very important due to the massive use of computer networks in different areas of society. Different techniques are usually used to carry out effective traffic control, allowing, among other things, to classify, predict and monitor network traffic. These techniques have evolved and are currently supported by artificial intelligence tools, which have made it possible to improve the results obtained with conventional techniques. This paper collects the different contributions made by the field of artificial intelligence to the improvement of these techniques and network management in general. The article describes the contributions made in aspects such as security, prediction, and classification of data traffic, as well as the optimization of routing in a computer network. [ABSTRACT FROM AUTHOR]

Traffic engineering in the Internet is responsible for the optimization and evaluation of the performance of IP networks to avoid congestion. One of the most accepted proposals is MPLS (Multiprotocol Label Switching), which provides an efficient paradigm for traffic optimization. Other proposals include the adjustment of link weights of interior gateway protocols (IGPs), the LISP protocol (Locator/ID Separation Protocol), and segment routing (SR). Currently, work is still being done on the topic and updates to existing recommendations are being proposed. [Extracted from the article]