RAILS联合优化跨域服务分解与提供商选择，提升5G网络SLA管理效率。
RAILS: Risk-Aware Iterated Local Search for Joint SLA Decomposition and Service Provider Management in Multi-Domain Networks
- 结合风险建模与迭代局部搜索，动态优化跨域资源分配。
- 在多域网络中实现近似最优的SLA分解，满足实时性要求。
- 适合需要弹性管理多服务商的5G/6G网络场景。
第五代移动通信技术（5G）将移动网络转变为多业务环境，亟需高效的网络切片以满足多样化的服务等级协议（SLA）。在多个网络域（每个域可能由不同服务提供商管理）中进行SLA分解，因对底层域状态缺乏实时可见性而面临重大挑战。本文提出一种风险感知的迭代局部搜索（RAILS）框架，通过在线风险建模与迭代局部搜索相结合，联合解决跨域网络中的SLA分解与服务提供商选择问题。该方法利用域控制器的历史反馈信息，构建混合整数非线性规划（MINLP）模型，并证明其为NP-hard问题。大量仿真表明，RAILS能实现接近最优的性能，为现代多域网络提供高效、实时的自适应SLA管理方案。
原文摘要 · Abstract (English)
The emergence of the fifth generation (5G) technology has transformed mobile networks into multi-service environments, necessitating efficient network slicing to meet diverse Service Level Agreements (SLAs). SLA decomposition across multiple network domains, each potentially managed by different service providers, poses a significant challenge due to limited visibility into real-time underlying domain conditions. This paper introduces Risk-Aware Iterated Local Search (RAILS), a novel risk model-driven meta-heuristic framework designed to jointly address SLA decomposition and service provider selection in multi-domain networks. By integrating online risk modeling with iterated local search principles, RAILS effectively navigates the complex optimization landscape, utilizing historical feedback from domain controllers. We formulate the joint problem as a Mixed-Integer Nonlinear Programming (MINLP) problem and prove its NP-hardness. Extensive simulations demonstrate that RAILS achieves near-optimal performance, offering an efficient, real-time solution for adaptive SLA management in modern multi-domain networks.
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