How AI, machine learning improve real-time communications traffic

Modern networks are causing a seismic shift in how real-time communications traverse IP networks to take the most optimal paths. Previous-generation techniques to manage traffic required static "if X, then Y" scenarios to be preprogrammed into networks on a hop-by-hop basis using legacy quality of service. But thanks to advancements in machine learning and AI, networks can take advantage of end-to-end network visibility and dynamic rerouting of data flows to dramatically improve real-time communications traffic performance and reliability. Legacy networks rely on traditional quality of service (QoS) to help improve the reliability of real-time communication data flows, such as voice and video. QoS uses a three-step process of identification, marking and policy enforcement to give preferential treatment to critical flows, including real-time streaming applications.