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.

翻译：云-边连续体中的服务放置需要在延迟、位置和策略要求等多重约束下，将应用组件分配给异构资源。现有方法依赖需要显式建模的优化模型或启发式算法，而神经方法缺乏透明性和形式化保证。本文提出一种基于Prolog技能的神经符号替代方案——该技能作为可复用接口，支持模式约束下的事实生成与查询，用于约束感知的服务放置。该技能使语言模型能够将放置意图结构化为符号事实、规则和查询，同时将验证与推理过程委托给Prolog。这一设计衔接了高层意图与形式化约束评估，使云-边环境中的放置决策具备可检查性与策略感知能力。