Low Complexity Approaches for End-to-End Latency Prediction
This work addresses the need for local, low-complexity KPI prediction to improve QoS in networking, but it is incremental as it builds on existing methods with minor trade-offs.
The paper tackled the problem of designing efficient and low-cost algorithms for end-to-end latency prediction in Software Defined Networks, achieving significantly lower wall time for training and inference with marginally worse accuracy compared to state-of-the-art global GNN solutions.
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.