  Software Defined Networks have opened the door to statistical and AI-based
techniques to improve efficiency of networking. Especially to ensure a certain
Quality of Service (QoS) for specific applications by routing packets with
awareness on content nature (VoIP, video, files, etc.) and its needs (latency,
bandwidth, etc.) to use efficiently resources of a network. Monitoring and
predicting various Key Performance Indicators (KPIs) at any level may handle
such problems while preserving network bandwidth. The question addressed in
this work is the design of efficient, low-cost adaptive algorithms for KPI
estimation, monitoring and prediction. We focus on end-to-end latency
prediction, for which we illustrate our approaches and results on data obtained
from a public generator provided after the recent international challenge on
GNN [12]. In this paper, we improve our previously proposed low-cost estimators
[6] by adding the adaptive dimension, and show that the performances are
minimally modified while gaining the ability to track varying networks.
