Low Complexity Approaches for End-to-End Latency Prediction - Archive ouverte HAL Access content directly
Conference Papers Year : 2022

Low Complexity Approaches for End-to-End Latency Prediction

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.
Fichier principal
Vignette du fichier
main.pdf (312.94 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03957811 , version 1 (30-01-2023)

Identifiers

Cite

Pierre Larrenie, Jean-François Bercher, Olivier Venard, Iyad Lahsen-Cherif. Low Complexity Approaches for End-to-End Latency Prediction. 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Oct 2022, Kharagpur, France. pp.1-6, ⟨10.1109/ICCCNT54827.2022.9984543⟩. ⟨hal-03957811⟩
35 View
64 Download

Altmetric

Share

Gmail Facebook X LinkedIn More