[Paper] A Neurosymbolic Prolog Skill for LLM-Driven Service Placement
Source: arXiv - 2606.11113v1
Overview
Service placement in the cloud-edge continuum requires assigning application components to heterogeneous resources under multiple constraints, including latency, locality, and policy requirements. Existing approaches rely on optimisation models or heuristics that require explicit modelling, while neural methods lack transparency and formal guarantees. This work proposes a neuro-symbolic alternative based on a Prolog skill, a reusable interface for schema-constrained fact generation and querying, for constraint-aware placement. The skill enables a language model to structure placement intent into symbolic facts, rules, and queries, while delegating validation and reasoning to Prolog. This design bridges high-level intent and formal constraint evaluation, enabling inspectable and policy-aware placement decisions in cloud-edge environments.
Key Contributions
This paper presents research in the following areas:
- cs.DC
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of cs.DC.
Authors
- Jacopo Massa
- Giuseppe Bisicchia
- Patrizio Dazzi
- Antonio Brogi
Paper Information
- arXiv ID: 2606.11113v1
- Categories: cs.DC
- Published: June 9, 2026
- PDF: Download PDF