  By decoupling substrate resources, network virtualization (NV) is a promising
solution for meeting diverse demands and ensuring differentiated quality of
service (QoS). In particular, virtual network embedding (VNE) is a critical
enabling technology that enhances the flexibility and scalability of network
deployment by addressing the coupling of Internet processes and services.
However, in the existing deep neural networks (DNNs)-based works, the black-box
nature DNNs limits the analysis, development, and improvement of systems. For
example, in the industrial Internet of Things (IIoT), there is a conflict
between decision interpretability and the opacity of DNN-based methods. In
recent times, interpretable deep learning (DL) represented by deep neuro fuzzy
systems (DNFS) combined with fuzzy inference has shown promising
interpretability to further exploit the hidden value in the data. Motivated by
this, we propose a DNFS-based VNE algorithm that aims to provide an
interpretable NV scheme. Specifically, data-driven convolutional neural
networks (CNNs) are used as fuzzy implication operators to compute the
embedding probabilities of candidate substrate nodes through entailment
operations. And, the identified fuzzy rule patterns are cached into the weights
by forward computation and gradient back-propagation (BP). Moreover, the fuzzy
rule base is constructed based on Mamdani-type linguistic rules using
linguistic labels. In addition, the DNFS-driven five-block structure-based
policy network serves as the agent for deep reinforcement learning (DRL), which
optimizes VNE decision-making through interaction with the environment.
Finally, the effectiveness of evaluation indicators and fuzzy rules is verified
by simulation experiments.
