Multi-domain Network Slice Partitioning: A Graph Neural Network Algorithm

TL;DR

This paper introduces a Graph Neural Network-based framework for efficient multi-domain network slice partitioning, balancing inter- and intra-domain costs while optimizing load distribution to enhance network performance.

Contribution

It presents a novel GNN-based algorithm for multi-domain slice partitioning that reduces plan generation time and improves load balancing compared to traditional methods.

Findings

Significantly faster plan generation with GNN solver

Effective load balancing across multiple domains

Improved network performance through optimized partitioning

Abstract

In the context of multi-domain network slices, multiple domains need to work together to provide a service. The problem of determining which part of the service fits within which domain is referred to as slice partitioning. The partitioning of multi-domain network slices poses a challenging problem, particularly when striving to strike the right balance between inter-domain and intra-domain costs, as well as ensuring optimal load distribution within each domain. To approach the optimal partition solution while maintaining load balance between domains, a framework has been proposed. This framework not only generates partition plans with various characteristics but also employs a Graph Neural Network solver, which significantly reduces the plan generation time. The proposed approach is promising in generating partition plans for multi-domain network slices and is expected to improve the overall performance of the network.