Routing is, arguably, the most fundamental task in computer networking, and the most extensively studied one. A key challenge for routing in real-world environments is the need to contend with uncertainty about future traffic demands. We present a new approach to routing under demand uncertainty: tackling this challenge as stochastic optimization, and employing deep learning to learn complex patterns in traffic demands. We show that our method provably converges to the global optimum in well-studied theoretical models of multicommodity flow. We exemplify the practical usefulness of our approach by zooming in on the real-world challenge of traffic engineering (TE) on wide-area networks (WANs). Our extensive empirical evaluation on real-world traffic and network topologies establishes that our approach's TE quality almost matches that of an (infeasible) omniscient oracle, outperforming previously proposed approaches, and also substantially lowers runtimes.

翻译：路由堪称计算机网络中最基础且研究最广泛的任务。现实环境中路由面临的关键挑战在于需应对未来流量需求的不确定性。我们提出一种应对需求不确定性路由问题的新方法：将该挑战视为随机优化问题，并运用深度学习学习流量需求的复杂模式。理论证明，在经典多商品流理论模型中，我们的方法可收敛至全局最优解。通过聚焦广域网流量工程这一真实世界挑战，我们阐明了该方法的实际效用。基于真实流量与网络拓扑的广泛实证评估表明，该方法在工程性能上近乎达到（不可实现的）全知先知水平，不仅全面超越既有方案，同时显著降低了运行时耗。