While many deep learning (DL)-based networking systems have demonstrated
superior performance, the underlying Deep Neural Networks (DNNs) remain
blackboxes and stay uninterpretable for network operators. The lack of
interpretability makes DL-based networking systems prohibitive to deploy in
practice. In this paper, we propose Metis, a framework that provides
interpretability for two general categories of networking problems spanning
local and global control. Accordingly, Metis introduces two different
interpretation methods based on decision tree and hypergraph, where it converts
DNN policies to interpretable rule-based controllers and highlight critical
components based on analysis over hypergraph. We evaluate Metis over several
state-of-the-art DL-based networking systems and show that Metis provides
human-readable interpretations while preserving nearly no degradation in
performance. We further present four concrete use cases of Metis, showcasing
how Metis helps network operators to design, debug, deploy, and ad-hoc adjust
DL-based networking systems.

本研究提出了 Metis 框架，改善了深度神经网络可解释性问题，通过决策树和超图等不同的解释方法，将 DNN 策略转换成易于理解的基于规则的控制器，并通过超图分析来突出重要组件。Metis 可帮助网络运营商设计、调试、部署和即时调整 DL 网络系统。