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 works, the black-box nature of deep neural networks (DNNs) limits the analysis, development, and improvement of systems. 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). In addition, the fuzzy rule base is constructed based on Mamdani-type linguistic rules using linguistic labels. Finally, the effectiveness of evaluation indicators and fuzzy rules is verified by experiments.

翻译：通过解耦底层资源，网络虚拟化（NV）是满足多样化需求并确保差异化服务质量（QoS）的一种有前景的解决方案。特别地，虚拟网络嵌入（VNE）作为一项关键使能技术，通过解决互联网进程与服务的耦合问题，增强了网络部署的灵活性和可扩展性。然而，在现有工作中，深度神经网络（DNN）的黑箱特性限制了系统的分析、开发与改进。近年来，以深度神经模糊系统（DNFS）结合模糊推理为代表的可解释深度学习（DL）展现出良好的可解释性，能够进一步挖掘数据中的隐藏价值。受此启发，我们提出一种基于DNFS的VNE算法，旨在提供可解释的NV方案。具体而言，数据驱动的卷积神经网络（CNN）作为模糊蕴含算子，通过蕴含运算计算候选底层节点的嵌入概率。同时，通过前向计算与梯度反向传播（BP），将识别的模糊规则模式缓存至权重中。此外，基于Mamdani型语言规则，利用语言标签构建模糊规则库。最后，通过实验验证了评估指标与模糊规则的有效性。