# Low Complexity Approaches for End-to-End Latency Prediction

arXiv: https://arxiv.org/abs/2302.00004 · HTML: https://mlsanity.com/paper/2302.00004/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 31 Jan 2023.

## Builds on (most cited first, 7 of 7)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [A Gentle Introduction to Deep Learning for Graphs](https://mlsanity.com/paper/1912.12693/) (2019)
- [Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN](https://mlsanity.com/paper/1901.08113/) (2019)
- [The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks](https://mlsanity.com/paper/2107.12433/) (2021)
- [Stringer: Balancing Latency and Resource Usage in Service Function Chain Provisioning](https://mlsanity.com/paper/1604.08618/) (2016)
- [SDN-enabled Tactical Ad Hoc Networks: Extending Programmable Control to the Edge](https://mlsanity.com/paper/1801.02909/) (2018)
- [A Survey on Software Defined Networking: Architecture for Next Generation Network](https://mlsanity.com/paper/2001.10165/) (2020)

## Abstract (arXiv)

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.