Driving operational efficiency in mobile networks using deep traffic analytics
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As mobile operators initiate the disaggregation of their Radio Access Networks (RANs), and as network functions undergo virtualization, it becomes crucial to have real-time and predictive insights into the network usage per service. Such information is vital for managing the compute resources that power the infrastructure, for enhancing efficiency, and for reducing energy consumption. Overprovisioning is a costly endeavor, and current reactive management strategies rely on data that’s typically available offline and hence too late to be of practical use.
In this presentation, I will elucidate how Net AI leverages AI’s capabilities to forecast mobile network traffic with remarkable precision. We employ these predictions to dynamically scale compute and capacity resources based on their real-time need, aligning with our vision of zero-touch network management.