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). 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 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保障差异化服务质量（QoS）的一种有前景的解决方案。其中，虚拟网络嵌入（VNE）通过解决互联网进程与服务的耦合问题，成为提升网络部署灵活性与可扩展性的关键使能技术。然而，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）为代表的可解释深度学习（DL）结合模糊推理，展现出利用数据中隐藏价值的可观可解释性。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，采用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；通过前向计算与梯度反向传播（BP），将辨识出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。进而，由DNFS驱动的五块结构策略网络作为深度强化学习（DRL）的智能体，通过与环境交互优化VNE决策。最后，通过实验验证了评估指标与模糊规则的有效性。