RAILS: Risk-Aware Iterated Local Search for Joint SLA Decomposition and Service Provider Management in Multi-Domain Networks

2025-02-10

AI Summary

To address the challenges of cross-domain SLA decomposition and service provider coordination in 5G multi-domain networks—particularly under low inter-domain state visibility and stringent real-time requirements—this paper proposes the Risk-Aware Iterative Local Search (RAILS) framework, jointly optimizing network slice demand decomposition and cross-domain service provider selection. We first formulate the problem as a Mixed-Integer Nonlinear Program (MINLP) and prove its NP-hardness. RAILS innovatively integrates online risk modeling with an iterative local search mechanism, dynamically adapting to evolving domain conditions via historical feedback from multi-domain controllers. Simulation results demonstrate that RAILS converges to near-optimal solutions within milliseconds, improves cross-domain resource matching efficiency by 32.7%, enhances service reliability by 28.4%, and significantly strengthens real-time, adaptive SLA assurance capabilities.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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.

Problem

• SLA decomposition
• service provider selection
• multi-domain networks

Innovation

• Risk-Aware Iterated Local Search
• Mixed-Integer Nonlinear Programming
• Multi-Domain SLA Management