# Is Machine Learning Ready for Traffic Engineering Optimization?

arXiv: https://arxiv.org/abs/2109.01445 · HTML: https://mlsanity.com/paper/2109.01445/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 52 references that are in the map.
- First posted on arXiv 3 Sep 2021.

## Builds on (most cited first, 15 of 15)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Interaction Networks for Learning about Objects, Relations and Physics](https://mlsanity.com/paper/1612.00222/) (2016)
- [Graph Convolutional Reinforcement Learning](https://mlsanity.com/paper/1810.09202/) (2018)
- [Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case](https://mlsanity.com/paper/1910.07421/) (2019)
- [Experience-driven Networking: A Deep Reinforcement Learning based Approach](https://mlsanity.com/paper/1801.05757/) (2018)
- [GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms](https://mlsanity.com/paper/2001.02498/) (2019)
- [Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN](https://mlsanity.com/paper/1901.08113/) (2019)
- [Machine Learning for Networking: Workflow, Advances and Opportunities](https://mlsanity.com/paper/1709.08339/) (2017)
- [A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization](https://mlsanity.com/paper/1709.07080/) (2017)
- [Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication](https://mlsanity.com/paper/2004.00470/) (2020)
- [REPETITA: Repeatable Experiments for Performance Evaluation of Traffic-Engineering Algorithms](https://mlsanity.com/paper/1710.08665/) (2017)

## Built on by (most cited first, 8 of 8)

- [MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering](https://mlsanity.com/paper/2303.18157/) (2023)
- [Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passing](https://mlsanity.com/paper/2402.05027/) (2024)
- [Proximal Policy Optimization with Graph Neural Networks for Optimal Power Flow](https://mlsanity.com/paper/2212.12470/) (2022)
- [A Graph Attention Learning Approach to Antenna Tilt Optimization](https://mlsanity.com/paper/2112.14843/) (2021)
- [FERN: Leveraging Graph Attention Networks for Failure Evaluation and Robust Network Design](https://mlsanity.com/paper/2305.19153/) (2023)
- [Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks](https://mlsanity.com/paper/2112.12321/) (2021)
- [Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary Strategies](https://mlsanity.com/paper/2202.00360/) (2022)
- [Traffic Engineering in Large-scale Networks with Generalizable Graph Neural Networks](https://mlsanity.com/paper/2503.24203/) (2025)

## Abstract (arXiv)

Traffic Engineering (TE) is a basic building block of the Internet. In this paper, we analyze whether modern Machine Learning (ML) methods are ready to be used for TE optimization. We address this open question through a comparative analysis between the state of the art in ML and the state of the art in TE. To this end, we first present a novel distributed system for TE that leverages the latest advancements in ML. Our system implements a novel architecture that combines Multi-Agent Reinforcement Learning (MARL) and Graph Neural Networks (GNN) to minimize network congestion. In our evaluation, we compare our MARL+GNN system with DEFO, a network optimizer based on Constraint Programming that represents the state of the art in TE. Our experimental results show that the proposed MARL+GNN solution achieves equivalent performance to DEFO in a wide variety of network scenarios including three real-world network topologies. At the same time, we show that MARL+GNN can achieve significant reductions in execution time (from the scale of minutes with DEFO to a few seconds with our solution).