  Thanks to evolving cellular telecommunication networks, providers can deploy
a wide range of services. Soon, 5G mobile networks will be available to handle
all types of services and applications for vast numbers of users through their
mobile equipment. To effectively manage new 5G systems, end-to-end (E2E)
performance analysis and optimization will be key features. However, estimating
the end-user experience is not an easy task for network operators. The amount
of end-user performance information operators can measure from the network is
limited, complicating this approach. Here we explore the calculation of service
metrics [known as key quality indicators (KQIs)] from classic low-layer
measurements and parameters. We propose a complete machine-learning (ML)
modeling framework. This system's low-layer metrics can be applied to measure
service-layer performance. To assess the approach, we implemented and evaluated
the proposed system on a real cellular network testbed.
