Intelligent Network Traffic Control with AI and Machine Learning

Given the rising complexity in communication networks and data traffic, novel strategies are badly needed to handle the effective transmission of data, especially in a broadcasting scenario where reliability comes into prime focus. The paper discusses how AI and ML can particularly be a panacea for solving such challenges. We start with traditional approaches like static routing and traffic shaping, which already aren't relevant in the face of more contemporary AI based solutions. AI and ML algorithms can predict congestion on the network and automatically find the most effective routes so that efficiency is optimized without any human intervention. To tackle this issue more concretely, several use cases from the telecommunications and content distribution networks are illustrated regarding how AI/ML is already making a difference in optimizing bandwidth, eliminating lag times, and providing stable internet connections for services such as video calls. However, AI/ML systems challenge the control of gigantic data and obtaining user's trust. Tomorrow, we find the potential for AI to autonomously run networks, particularly when new technologies like 5G and 6G are released.