AI 中文总结
针对 5G 后及 6G 网络对 vRAN 架构的需求，提出集成框架，通过联合优化功能放置与光资源分配来最大化运营商利润，将问题建模为 ILP 模型，开发算法求解，实验表明共享备份变体有利润和 CPU 使用率优势。
AI 中文摘要
5G 之后及 6G 网络的快速发展，推动了对灵活、可靠且经济高效的虚拟无线接入网络（vRAN）架构的需求，以支持增强移动宽带（eMBB）、超可靠低延迟通信（URLLC）和大规模机器类型通信（mMTC）等异构服务。未来的分布式 RAN 系统预计将严重依赖网络切片、功能拆分灵活性和光前传基础设施。本文提出了一个在分布式 vRAN 环境中进行可靠、感知切片和功能拆分感知的虚拟网络功能（VNF）放置并进行光路配置的集成框架。该方法通过在延迟、处理、带宽和可用性约束下联合优化功能放置和光资源分配，最大化移动网络运营商的利润。将问题表述为整数线性规划（ILP）模型，有两种变体。为解决 ILP 复杂性，开发了启发式算法和基于遗传算法（GA）的元启发式算法，能实时产生接近最优的解决方案。对多达 128 个节点的拓扑进行的广泛评估表明，共享备份变体的利润比非共享备份高出 18%，同时归一化 CPU 使用率比非共享备份低 5 - 10%。
英文摘要
The rapid evolution of beyond-5G and emerging 6G networks is driving the need for flexible, reliable, and cost-efficient virtualized Radio Access Network (vRAN) architectures capable of supporting heterogeneous services such as enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and Massive Machine-Type Communication (mMTC). Future disaggregated RAN systems are expected to rely heavily on network slicing, functional split flexibility, and optical x-haul infrastructures to support stringent performance, scalability, and availability requirements. In this paper, we present an integrated framework for reliable, slice-aware, and functional split-aware Virtual Network Function (VNF) placement with lightpath provisioning in disaggregated vRAN environments. The proposed approach maximizes mobile network operators' profit by jointly optimizing function placement and optical resource allocation under latency, processing, bandwidth, and availability constraints. We formulate the problem as an Integer Linear Programming (ILP) model with two variants: one that employs unshared backups and another that uses a more cost-efficient shared backup scheme. To address ILP complexity, we develop a heuristic algorithm and a Genetic Algorithm (GA)-based metaheuristic that yields near-optimal solutions in real time. Extensive evaluations on topologies up to 128 nodes show that shared backup variants yield up to 18% higher profit, while maintaining up to 5-10% lower normalized CPU usage than unshared counterparts.
