Service Function Chain (SFC) provisioning stands as a pivotal technology in the realm of 5G and future networks. Its essence lies in orchestrating VNFs (Virtual Network Functions) in a specified sequence for different types of SFC requests. Efficient SFC provisioning requires fast, reliable, and automatic VNFs' placements, especially in a network where massive amounts of SFC requests are generated having ultra-reliable and low latency communication (URLLC) requirements. Although much research has been done in this area, including Artificial Intelligence (AI) and Machine Learning (ML)-based solutions, this work presents an advanced Deep Reinforcement Learning (DRL)-based simulation model for SFC provisioning that illustrates a realistic environment. The proposed simulation platform can handle massive heterogeneous SFC requests having different characteristics in terms of VNFs chain, bandwidth, and latency constraints. Also, the model is flexible to apply to networks having different configurations in terms of the number of data centers (DCs), logical connections among DCs, and service demands. The simulation model components and the workflow of processing VNFs in the SFC requests are described in detail. Numerical results demonstrate that using this simulation setup and proposed algorithm, a realistic SFC provisioning can be achieved with an optimal SFC acceptance ratio while minimizing the E2E latency and resource consumption.

翻译：服务功能链（SFC）配置是5G及未来网络领域的一项关键技术，其核心在于为不同类型的SFC请求按特定顺序编排虚拟网络功能（VNF）。高效的SFC配置需要快速、可靠且自动化的VNF部署，尤其在产生海量具有超可靠低时延通信（URLLC）需求的SFC请求的网络中。尽管该领域已有大量研究，包括基于人工智能（AI）和机器学习（ML）的解决方案，但本研究提出了一种基于深度强化学习（DRL）的先进SFC配置仿真模型，该模型呈现了真实网络环境。所提出的仿真平台能够处理具有不同VNF链、带宽和时延约束特征的大规模异构SFC请求。同时，该模型可灵活适用于具有不同数据中心（DC）数量、DC间逻辑连接和服务需求的网络配置。本文详细阐述了仿真模型的组件及处理SFC请求中VNF的工作流程。数值结果表明，采用该仿真设置与所提算法，可在优化SFC接受率的同时实现端到端时延与资源消耗最小化的现实SFC配置。