AGORA: Agentic Green Orchestration Architecture for Beyond 5G Networks

2026-02-08

AI Summary

This work addresses the lack of scalable mechanisms in current mobile networks to effectively translate sustainability goals into energy-efficient strategies. The authors propose a tool-augmented, lightweight large language model (LLM) agent embedded within the network control loop that interprets natural-language sustainability intents and converts them into telemetry-driven, energy-aware traffic scheduling commands to orchestrate User Plane Function (UPF) operations in an environmentally conscious manner. This approach represents the first integration of natural-language intent-driven control with edge networking, enabling non-zero migration under the resource constraints of Multi-access Edge Computing (MEC). Experimental results demonstrate a strong coupling between control latency and energy consumption, confirming that the lightweight LLM can accurately execute policies with low overhead while maintaining effective migration capabilities even under stressed MEC conditions.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Efficient ML / Green AI
• Multiagent Systems: Agent Communication

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Effective management and operational decision-making for complex mobile network systems present significant challenges, particularly when addressing conflicting requirements such as efficiency, user satisfaction, and energy-efficient traffic steering. The literature presents various approaches aimed at enhancing network management, including the Zero-Touch Network (ZTN) and Self-Organizing Network (SON); however, these approaches often lack a practical and scalable mechanism to consider human sustainability goals as input, translate them into energy-aware operational policies, and enforce them at runtime. In this study, we address this gap by proposing the AGORA: Agentic Green Orchestration Architecture for Beyond 5G Networks. AGORA embeds a local tool-augmented Large Language Model (LLM) agent in the mobile network control loop to translate natural-language sustainability goals into telemetry-grounded actions, actuating the User Plane Function (UPF) to perform energy-aware traffic steering. The findings indicate a strong latency-energy coupling in tool-driven control loops and demonstrate that compact models can achieve a low energy footprint while still facilitating correct policy execution, including non-zero migration behavior under stressed Multi-access Edge Computing (MEC) conditions. Our approach paves the way for sustainability-first, intent-driven network operations that align human objectives with executable orchestration in Beyond-5G infrastructures.

Problem

• sustainability
• energy-efficient traffic steering
• Beyond 5G
• network orchestration
• operational decision-making

Innovation

• Agentic AI
• Green Networking
• Large Language Model (LLM)
• Intent-based Orchestration
• Energy-aware Traffic Steering