Low Complexity Approaches for End-to-End Latency Prediction. (arXiv:2302.00004v1 [cs.AI])

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. 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 and low-cost algorithms for KPI prediction, implementable at the
local level. We focus on end-to-end latency prediction, for which we illustrate
our approaches and results on a public dataset from the recent international
challenge on GNN [1]. We propose several low complexity, locally implementable
approaches, achieving significantly lower wall time both for training and
inference, with marginally worse prediction accuracy compared to
state-of-the-art global GNN solutions.