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.

The computing continuum aims to break the isolation of edge and cloud computing, creating a smooth and heterogeneous infrastructure surface for deploying applications. However, the continuum is practically fragmented, with infrastructure managed in isolation by various parties in a vertical dimension, i.e., edge, fog, and cloud layers, and horizontally, i.e., computing, network, and storage domains. This scenario negatively impacts the fulfillment of the application objectives. We propose inCoord, an intent-aware solution that enables the creation of a unified computing continuum by coordinating its instances to fulfill application objectives. Each instance represents a cluster of (compute, storage, or network) nodes with their own manager component. Traditionally, systems take low-level actions on these instances. In contrast, inCoord learns their emerging behaviors and adapts their managers’ objectives to fulfill the application’s intents. Here, through a Reinforcement-Learning-based Proof of Concept, we show the potential of this system to understand emerging behavior and manage multi-domain, independently managed instances.