Large Language Models meet Network Slicing Management and Orchestration

TL;DR

This paper envisions integrating Large Language Models with multi-agent systems to enhance network slicing management, aiming to improve translation of user intent, resource allocation, and lifecycle management in complex, multi-domain networks.

Contribution

It introduces a novel framework combining LLMs and multi-agent systems for network slicing orchestration, addressing current limitations in handling complex service demands.

Findings

Proposes a framework integrating LLMs with MANO systems

Highlights potential for improved automation and collaboration across domains

Discusses challenges and solutions for implementation

Abstract

Network slicing, a cornerstone technology for future networks, enables the creation of customized virtual networks on a shared physical infrastructure. This fosters innovation and agility by providing dedicated resources tailored to specific applications. However, current orchestration and management approaches face limitations in handling the complexity of new service demands within multi-administrative domain environments. This paper proposes a future vision for network slicing powered by Large Language Models (LLMs) and multi-agent systems, offering a framework that can be integrated with existing Management and Orchestration (MANO) frameworks. This framework leverages LLMs to translate user intent into technical requirements, map network functions to infrastructure, and manage the entire slice lifecycle, while multi-agent systems facilitate collaboration across different administrative domains. We also discuss the challenges associated with implementing this framework and potential solutions to mitigate them.