Generative Intent Prediction Agentic AI empowered Edge Service Function Chain Orchestration

TL;DR

This paper introduces a proactive edge service orchestration framework using a generative diffusion model to predict user intents from natural language, improving network management in dynamic environments.

Contribution

It presents a novel intent prediction model based on a generative diffusion approach and integrates it into an edge SFC orchestration framework for proactive network management.

Findings

GIPA outperforms baseline methods in dynamic scenarios.

The intent space effectively maps natural language to resource demands.

Proactive orchestration improves network efficiency and user experience.

Abstract

With the development of artificial intelligence (AI), Agentic AI (AAI) based on large language models (LLMs) is gradually being applied to network management. However, in edge network environments, high user mobility and implicit service intents pose significant challenges to the passive and reactive management of traditional AAI. To address the limitations of existing approaches in handling dynamic demands and predicting users' implicit intents, in this paper we propose an edge service function chain (SFC) orchestration framework empowered by a Generative Intent Prediction Agent (GIPA). Our GIPA aims to shift the paradigm from passive execution to proactive prediction and orchestration. First, we construct a multidimensional intent space that includes functional preferences, QoS sensitivity, and resource requirements, enabling the mapping from unstructured natural language to quantifiable physical resource demands. Second, to cope with the complexity and randomness of intent sequences, we design an intent prediction model based on a Generative Diffusion Model (GDM), which reconstructs users' implicit intents from multidimensional context through a reverse denoising process. Finally, the predicted implicit intents are embedded as global prompts into the SFC orchestration model to guide the network in proactively and ahead-of-time optimizing SFC deployment strategies. Experiment results show that GIPA outperforms existing baseline methods in highly concurrent and highly dynamic scenarios.