The goal of UC6-NSTAT is to provide a network monitoring service that can be used to increase the efficiency of other NetApps through providing distributed predictions of QoS and in general of network conditions at various locations. This NetApp provides an overview of the status of network components or virtual network functions and draws conclusions and predictions with respect to the performance of the monitored components. It utilizes network communications to deliver predictions of the network quality to a central computation entity at the MEC server. This NetApp has the goal to minimize the data collection effort through utilizing a distributed Machine Learning approach, i.e., instead of collecting large amounts of network monitoring data to be centrally analysed, the ML analysis/prediction model is distributed on the VNFs located at the RSUs and the vehicle OBUs. The goal of the ML model is (1) to learn data traffic patterns for data traffic prediction, (2) to learn network condition models to provide QoS predictions, and (3) to learn to distinguish between normal and abnormal network behaviours to detect and predict faults.