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.

翻译：软件定义网络为基于统计和人工智能的技术打开了提高网络效率的大门。尤其通过根据数据内容性质（如VoIP、视频、文件等）及其需求（如延迟、带宽等）进行路由感知，从而高效利用网络资源，确保特定应用的特定服务质量（QoS）。在各个层面监控和预测各种关键性能指标（KPI）可以在保持网络带宽的同时处理此类问题。本文研究的关键问题是如何设计高效、低成本的KPI估计、监控与预测的自适应算法。我们聚焦于端到端延迟预测，并基于近期国际图神经网络挑战赛[12]中提供的公开生成器数据，展示我们的方法与结果。本文通过增加自适应维度，改进了我们先前提出的低成本估计器[6]，并证明在获得跟踪可变网络能力的同时，其性能仅受到极小影响。