Intent-to-Learning Translation for Computing Continuum Management

Abstract

This paper offers a solution to generate concrete goals for automating the fulfillment of user intents in the computing continuum. The computing continuum guarantees a flexible infrastructure for services at the cost of more complex handling. Our proposed method innovates the state of the art, helping build performative automated strategies through the translation of service owner intents into concrete targets. We improve on existing intent-based systems by offering support for multi-domain infrastructures. Furthermore, we go beyond current computing continuum management solutions, offering full automation by generating concrete targets for the continuum of automated agents. We achieve that through a multi-agent system built on Large Language Models (LLMs) that translates high-level business intents into executable Reinforcement Learning (RL) environments. By leveraging infrastructure representations in the form of Knowledge Graphs, the framework identifies which system components require adaptation and estimates the target metric values needed to fulfill the intent. We evaluate it on a realistic use case with promising results. We can deploy a fully working RL agent to manage network and computing resources, achieving a success rate higher than 80% after preliminary training.