Opportunities

The PhD student will investigate suitable methods and edge node component architecture capable of estimating the QoE as perceived per target users in access networks close to / served from a MEC platform where QoE estimation is carried. In addition to the engineering challenges of the MEC-based QoE estimator, multiple research questions are still open around the limits and applicability of ML to softwarized networks and end-to-end service lifecycle management. How to port techniques from one networking scenario, service, and time frame to another one in an effective manner is only one of the many-fold limitations in the state of the art on ML/AI for networking. Research questions on ML for CCL include QoE inference and prediction based on network-level data collection, as well as the ability to perform root cause analysis of service degradation, among many applications of ML for IoV