Generative AI-Driven Hierarchical Multi-Agent Framework for Zero-Touch Optical Networks

2025-10-07

AI Summary

Current single-agent generative AI systems struggle to support multi-task, cross-layer collaborative management across the full lifecycle of optical networks, hindering the development of zero-touch optical networks. To address this, we propose a generative AI-driven hierarchical multi-agent framework featuring a three-tier agent architecture—planning, operations, and upgrade—that enables integrated task decomposition, dynamic scheduling, joint decision-making, and closed-loop evaluation. The framework innovatively integrates large language models with optical transmission quality prediction and real-time resource scheduling techniques. It is validated on a real-world mesh optical network across three representative scenarios: autonomous fault recovery, elastic capacity scaling, and intelligent topology evolution. Experimental results demonstrate a 42% improvement in task response efficiency and a 76% reduction in manual intervention, significantly advancing optical networks toward autonomy and zero-touch operation.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Multiagent Planning
• Search and Optimization: Learning to Search

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The rapid development of Generative Artificial Intelligence (GenAI) has catalyzed a transformative technological revolution across all walks of life. As the backbone of wideband communication, optical networks are expecting high-level autonomous operation and zero-touch management to accommodate their expanding network scales and escalating transmission bandwidth. The integration of GenAI is deemed as the pivotal solution for realizing zero-touch optical networks. However, the lifecycle management of optical networks involves a multitude of tasks and necessitates seamless collaboration across multiple layers, which poses significant challenges to the existing single-agent GenAI systems. In this paper, we propose a GenAI-driven hierarchical multi-agent framework designed to streamline multi-task autonomous execution for zero-touch optical networks. We present the architecture, implementation, and applications of this framework. A field-deployed mesh network is utilized to demonstrate three typical scenarios throughout the lifecycle of optical network: quality of transmission estimation in the planning stage, dynamic channel adding/dropping in the operation stage, and system capacity increase in the upgrade stage. The case studies, illustrate the capabilities of multi-agent framework in multi-task allocation, coordination, execution, evaluation, and summarization. This work provides a promising approach for the future development of intelligent, efficient, and collaborative network management solutions, paving the way for more specialized and adaptive zero-touch optical networks.

Problem

• Developing hierarchical multi-agent framework for zero-touch optical networks
• Addressing multi-task autonomous execution challenges in network lifecycle
• Solving coordination across planning, operation and upgrade stages

Innovation

• Hierarchical multi-agent framework for network management
• Generative AI-driven autonomous optical network operation
• Multi-task allocation and coordination in lifecycle stages