LARRI: Learning-based Adaptive Range Routing for Highly Dynamic Traffic in WANs

Minghao Ye, Junjie Zhang, Zehua Guo, H. Jonathan Chao

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Traffic Engineering (TE) has been widely used by network operators to improve network performance and provide better service quality to users. One major challenge for TE is how to generate good routing strategies adaptive to highly dynamic future traffic scenarios. Unfortunately, existing works could either experience severe performance degradation under unexpected traffic fluctuations or sacrifice performance optimality for guaranteeing the worst-case performance when traffic is relatively stable. In this paper, we propose LARRI, a learning-based TE to predict adaptive routing strategies for future unknown traffic scenarios. By learning and predicting a routing to handle an appropriate range of future possible traffic matrices, LARRI can effectively realize a trade-off between performance optimality and worst-case performance guarantee. This is done by integrating the prediction of future demand range and the imitation of optimal range routing into one step. Moreover, LARRI employs a scalable graph neural network architecture to greatly facilitate training and inference. Extensive simulation results on six real-world network topologies and traffic traces show that LARRI achieves near-optimal load balancing performance in future traffic scenarios with up to 43.3% worst-case performance improvement over state-of-the-art baselines, and also provides the lowest end-to-end delay under dynamic traffic fluctuations.

Original languageEnglish (US)
Title of host publicationINFOCOM 2023 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350334142
StatePublished - 2023
Event42nd IEEE International Conference on Computer Communications, INFOCOM 2023 - Hybrid, New York City, United States
Duration: May 17 2023May 20 2023

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X


Conference42nd IEEE International Conference on Computer Communications, INFOCOM 2023
Country/TerritoryUnited States
CityHybrid, New York City


  • graph neural networks
  • range routing
  • routing prediction
  • supervised learning
  • traffic engineering

ASJC Scopus subject areas

  • General Computer Science
  • Electrical and Electronic Engineering


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