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

TL;DR

This paper presents RAILS, a risk-aware meta-heuristic framework that optimizes SLA decomposition and provider management in multi-domain 5G networks, addressing real-time challenges with a novel risk model.

Contribution

Introduces RAILS, a risk-aware meta-heuristic that jointly optimizes SLA decomposition and provider selection using online risk modeling in multi-domain networks.

Findings

RAILS achieves near-optimal performance in simulations.

The joint problem is NP-hard, justifying the need for heuristic solutions.

RAILS provides an efficient, real-time adaptive SLA management approach.

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.