用神经网络生成可解释的路由嵌入，提升网络响应速度。
Can Neural Networks Provide Latent Embeddings for Telemetry-Aware Greedy Routing?
- 用消息传递网络将网络状态转为节点嵌入
- 无需计算全路径最短路，实现快速贪心选下一跳
- 能可视化网络事件如何影响路由决策
感知遥测的路由能够提升计算机网络在流量激增时的效率与响应能力。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，但因所提神经路由模块的黑箱特性，牺牲了路由决策的可解释性。本文提出 extit{Placer}，一种新型算法，使用消息传递网络将网络状态转换为节点隐含嵌入。这些嵌入支持快速贪心下一跳路由，避免直接求解全对最短路径问题，并可可视化特定网络事件如何影响路由决策。
原文摘要 · Abstract (English)
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.
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