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Emerging applications such as the metaverse, telesurgery or cloud computingrequire increasingly complex operational demands on networks (e.g.,ultra-reliable low latency). Likewise, the ever-faster traffic dynamics willdemand network control mechanisms that can operate at short timescales (e.g.,sub-minute). In this context, Traffic Engineering (TE) is a key component toefficiently 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 onGraph Neural Networks (GNN) and differentiable programming. Thanks to itsinternal GNN model, RBB builds an end-to-end differentiable function of thetarget TE problem (MinMaxLoad). This enables fast TE optimization via gradientdescent. In our evaluation, we show the potential of RBB to optimize OSPF-basedrouting ($\approx$25\% of improvement with respect to default OSPFconfigurations). Moreover, we test the potential of RBB as an initializer ofcomputationally-intensive TE solvers. The experimental results show promisingprospects for accelerating this type of solvers and achieving efficient onlineTE optimization.

Fast Traffic Engineering by Gradient Descent with Learned Differentiable Routing