Virtualization technology, Network Function Virtualization (NFV), gives flexibility to communication and 5G core network technologies for dynamic and efficient resource allocation while reducing the cost and dependability of the physical infrastructure. In the NFV context, Service Function Chain (SFC) refers to the ordered arrangement of various Virtual Network Functions (VNFs). To provide an automated SFC provisioning algorithm that satisfies high demands of SFC requests having ultra-reliable and low latency communication (URLLC) requirements, in the literature, Artificial Intelligence (AI) modules and Deep Reinforcement Learning (DRL) algorithms are investigated in detail. This research proposes a generative Variational Autoencoder (VAE) assisted advanced-DRL module for handling SFC requests in a dynamic environment where network configurations and request amounts can be changed. Using the hybrid approach, including generative VAE and DRL, the algorithm leverages several advantages, such as dimensionality reduction, better generalization on the VAE side, exploration, and trial-error learning from the DRL model. Results show that GenAI-assisted DRL surpasses the state-of-the-art model of DRL in SFC provisioning in terms of SFC acceptance ratio, E2E delay, and throughput maximization.

翻译：虚拟化技术，特别是网络功能虚拟化（NFV），为通信及5G核心网技术提供了灵活的动态高效资源分配能力，同时降低了物理基础设施的成本与依赖性。在NFV框架下，服务功能链（SFC）指多个虚拟网络功能（VNF）按序排列形成的链式结构。为满足具有超可靠低时延通信（URLLC）需求的高强度SFC请求，现有研究深入探讨了人工智能（AI）模块与深度强化学习（DRL）算法在自动化SFC配置中的应用。本研究提出一种基于生成式变分自编码器（VAE）辅助的增强型DRL模块，用于处理网络配置与请求量动态变化环境中的SFC请求。该融合生成式VAE与DRL的混合方法兼具多重优势：VAE端实现降维与泛化能力提升，DRL端则发挥探索与试错学习特性。实验结果表明，在SFC配置场景中，生成式人工智能辅助的DRL模型在SFC接受率、端到端时延及吞吐量最大化等指标上均优于当前最先进的DRL模型。