Telemetry-Aware routing promises to increase efficacy and responsiveness to traffic surges in computer networks. Recent research leverages Machine Learning to deal with the complex dependency between network state and routing, but sacrifices explainability of routing decisions due to the black-box nature of the proposed neural routing modules. We propose \emph{Placer}, a novel algorithm using Message Passing Networks to transform network states into latent node embeddings. These embeddings facilitate quick greedy next-hop routing without directly solving the all-pairs shortest paths problem, and let us visualize how certain network events shape routing decisions.

翻译：感知遥测的路由技术有望提升计算机网络应对流量激增的效能与响应能力。近期研究利用机器学习处理网络状态与路由间的复杂依赖关系，但由于所提出的神经路由模块具有黑箱特性，牺牲了路由决策的可解释性。我们提出一种新颖算法——\emph{Placer}，该算法利用消息传递网络将网络状态转化为潜在节点嵌入。这些嵌入无需直接求解全对最短路径问题即可实现快速的贪心下一跳路由，并使我们能够可视化特定网络事件如何影响路由决策。