Investigating Neurosymbolic AI for Intent-based Service Management

The increasing complexity and increasing demands of IT applications, especially in federated multi-cluster environments, pose significant challenges for service orchestration. To address these, Zero-Touch Service Management (ZSM) and intent-based management paradigms are gaining traction, allowing users to specify high-level goals rather than low-level configurations. However, current intent-driven approaches often rely on rigid Domain Specific Languages (DSLs) or graphic user interfaces, limiting expressiveness and usability. In this work, we propose a neurosymbolic intent-based platform that leverages Large Language Models (LLMs) for natural language intent ingestion and Answer Set Programming (ASP), a declarative programming paradigm used for solving complex combinatorial problems. The system translates natural language descriptions of microservice requirements into structured policies, enabling explainable service-to-cluster matching across federated Kubernetes environments. We validate our approach through experiments that evaluate both the syntactic correctness and efficiency of various LLMs in intent translation, as well as the computational time of the symbolic placement algorithm.