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Fast Traffic Engineering by Gradient Descent with Learned Differentiable Routing

Link to paper: [here]

Krzysztof Rusek, Paul Almasan, José Suárez-Varela, Piotr Chołda, Pere Barlet-Ros, Albert Cabellos-Aparicio

Contact: [email protected]

Abstract

Emerging applications such as the metaverse, telesurgery or cloud computing require increasingly complex operational demands on networks (e.g., ultra-reliable low latency). Likewise, the ever-faster traffic dynamics will demand network control mechanisms that can operate at short timescales (e.g., sub-minute). In this context, Traffic Engineering (TE) is a key component to efficiently control network traffic according to some performance goals (e.g., minimize network congestion).

This paper presents Routing By Backprop (RBB), a novel TE method based on Graph Neural Networks (GNN) and differentiable programming. Thanks to its internal GNN model, RBB builds an end-to-end differentiable function of the target TE problem (MinMaxLoad). This enables fast TE optimization via gradient descent. In our evaluation, we show the potential of RBB to optimize OSPF-based routing (≈25% of improvement with respect to default OSPF configurations). Moreover, we test the potential of RBB as an initializer of computationally-intensive TE solvers. The experimental results show promising prospects for accelerating this type of solvers and achieving efficient online TE optimization.

Try RBB on Colab

Take a look at our demo in colab.

Instructions to execute

This repository contains code used in the numerical experiments, and allow for reproductioin of our results.

Train

First we need to train the model for appropriate large dataset.

python3 python/sp.py --train_graphs=10 --test_graphs=2 --checkpoint_steps=2

Optimize

Once the model is trained, it can be used for network optimization. Below is the example for nsf network topology.

	mkdir -p out/$@
	PYTHONPATH=baselines:${PYTHONPATH} python3 python/optimize_sp.py \
		--num_opt 5 \
		--report out/$@/opt.csv \
		--num_sgd 1 \
		--nopmap

If you decide to apply the concepts presented or base on the provided code, please do refer our paper.

@INPROCEEDINGS{9964923,
  author={Rusek, Krzysztof and Almasan, Paul and Suárez-Varela, José and Chołda, Piotr and Barlet-Ros, Pere and Cabellos-Aparicio, Albert},
  booktitle={2022 18th International Conference on Network and Service Management (CNSM)}, 
  title={Fast Traffic Engineering by Gradient Descent with Learned Differentiable Routing}, 
  year={2022},
  volume={},
  number={},
  pages={359-363},
  doi={10.23919/CNSM55787.2022.9964923}}