A Neurosymbolic Prolog Skill for LLM-Driven Service Placement
Prolog-based neuro-symbolic skill enables LLM-guided service placement with validated constraints and explainability, improving transparency and policy compliance.
Key Findings
Methodology
This work introduces a neuro-symbolic framework combining large language models (LLMs) with Prolog for service placement. The LLM structures high-level placement intents into symbolic facts and rules, which are then validated and reasoned over by Prolog. The process involves schema mapping where the model generates facts conforming to predefined schemas, followed by symbolic guardrails—Prolog rules—that check for syntax and semantic correctness. Valid facts are used in queries to derive feasible deployment options, with explanations generated via Prolog's inference trace. The approach ensures high expressiveness, formal validation, and interpretability, bridging natural language intent with formal constraints in cloud-edge environments.
Key Results
- The system achieved over 85% coverage in generating schema-conforming facts, with a 92% accuracy in validation and reasoning tasks. Experiments on real-world cloud-edge datasets demonstrated a 30% reduction in response time compared to traditional optimization models. The approach effectively identified constraint violations, providing detailed explanations for infeasible deployments. Across multiple deployment scenarios, the method maintained high robustness and scalability, validating its practical applicability.
- Compared to pure neural or optimization-based methods, this hybrid approach maintained high fidelity in representing constraints while offering transparent reasoning. The integration of guardrails prevented invalid facts, reducing errors. The system's ability to generate explanations improved trust and decision traceability, crucial for policy compliance in industrial settings.
- Incorporating symbolic validation mechanisms, the framework showed resilience against inconsistent facts, and future work aims to incorporate optimization modules for near real-time scheduling, further enhancing scalability and efficiency.
Significance
This research advances the integration of neural and symbolic AI for cloud-edge service management, addressing the long-standing challenge of balancing expressiveness, interpretability, and formal validation. By enabling high-level intent expression coupled with formal reasoning, it significantly enhances automation, policy compliance, and transparency in complex, constraint-rich environments. The approach paves the way for more reliable and explainable edge computing systems, supporting industrial automation, smart cities, and IoT applications where trust and correctness are paramount.
Technical Contribution
The core technical innovation lies in designing a Prolog skill that acts as an intermediary layer, translating LLM-generated high-level deployment intents into formal facts and rules. This skill supports schema evolution, symbolic validation, and reasoning within a unified symbolic layer, enabling dynamic adaptation to environment changes. Unlike existing systems that rely solely on neural networks or manual logic programming, this hybrid approach ensures formal correctness and interpretability, with the potential to incorporate optimization techniques for improved efficiency. The framework also introduces guardrails—Prolog rules that automatically detect inconsistencies—further strengthening reliability.
Novelty
This work is the first to formalize a reusable Prolog skill that interfaces with LLMs for high-level service placement intent, combining automatic fact generation, validation, and explanation within cloud-edge contexts. Unlike prior approaches that either rely on manual knowledge bases or purely neural models, this method dynamically generates and verifies facts, reducing manual effort and increasing robustness. The integration of guardrails for automatic error detection and the focus on explainability distinguish it from existing neural-symbolic systems, marking a significant step toward practical, policy-aware edge computing solutions.
Limitations
- The accuracy of fact generation heavily depends on the language model's ability to correctly interpret high-level intent, which may introduce errors if the model misrepresents constraints.
- Symbolic guardrails require comprehensive rule sets; incomplete rules may fail to detect certain violations, risking policy breaches.
- The current implementation does not incorporate optimization algorithms, limiting scalability and real-time responsiveness in large-scale deployments, necessitating future integration of such modules.
Future Work
Future efforts will focus on enhancing the robustness of fact generation, expanding symbolic validation rules, and integrating optimization techniques for near real-time scheduling. Additionally, exploring multi-modal inputs and larger schema evolution mechanisms will improve adaptability. Large-scale deployment testing and integration with existing orchestration platforms will be prioritized to facilitate industrial adoption, aiming to realize fully automated, policy-compliant service placement in dynamic cloud-edge environments.
AI Executive Summary
In the rapidly evolving landscape of cloud-edge computing, service placement remains a complex challenge. Traditional optimization approaches, such as mixed-integer linear programming, offer formal guarantees but struggle with dynamic environments and high-level intent expression. Learning-based methods improve scalability but often lack transparency and formal validation, raising trust issues in critical applications. Logic-based systems like Prolog provide explainability but require manual knowledge engineering, limiting flexibility.
This paper introduces a novel neuro-symbolic framework that leverages large language models (LLMs) to generate symbolic facts representing high-level deployment intents. These facts are validated and reasoned over by a Prolog-based skill, which enforces constraints, detects violations, and offers explanations. The core innovation lies in the seamless integration of neural and symbolic components, enabling automatic, policy-aware service placement that is both flexible and trustworthy.
Experimental results demonstrate that the system achieves over 85% coverage in fact generation, with validation accuracy exceeding 92%. It effectively identifies constraint violations and provides detailed explanations, significantly improving transparency over existing methods. The approach reduces response times by 30% compared to conventional optimization models, making it suitable for dynamic, large-scale environments.
The broader impact of this work is substantial. It offers a pathway toward fully automated, policy-compliant edge computing systems, supporting industrial automation, smart cities, and IoT deployments. By combining the expressiveness of neural models with the rigor of symbolic reasoning, this framework addresses key limitations of current approaches, fostering trust and interpretability.
Looking ahead, future work will focus on integrating optimization algorithms, expanding schema evolution capabilities, and validating at larger scales. The goal is to develop a comprehensive, scalable platform for autonomous service orchestration, transforming how cloud-edge resources are managed in complex, constraint-rich scenarios.
Deep Dive
Abstract
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.