Low Complexity Approaches for End-to-End Latency Prediction

TL;DR

This paper introduces low-complexity, locally implementable algorithms for end-to-end latency prediction in Software Defined Networks, aiming to balance prediction accuracy with computational efficiency and real-time applicability.

Contribution

It presents novel low-cost algorithms for KPI prediction that are easy to implement locally, with competitive accuracy and significantly reduced training and inference times.

Findings

Achieved lower wall time for training and inference.

Maintained marginally worse accuracy than global GNN solutions.

Demonstrated effectiveness on a public dataset from an international challenge.

Abstract

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.