Fast Traffic Engineering by Gradient Descent with Learned Differentiable Routing

TL;DR

This paper introduces Routing By Backprop (RBB), a novel traffic engineering method using Graph Neural Networks and differentiable programming to enable fast, gradient-based optimization of network routing for improved performance.

Contribution

The paper presents RBB, a new TE approach leveraging GNNs and differentiable programming to optimize routing efficiently and serve as an initializer for traditional TE solvers.

Findings

RBB achieves approximately 25% improvement over default OSPF routing.

RBB can accelerate traditional TE solvers.

RBB shows promise for online traffic engineering optimization.

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 ($\approx$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.