意图引擎：面向计算连续体中意图驱动编排的自然语言意图翻译
Intent Engine: Natural-Language Intent Translation for Intent-Driven Orchestration in the Compute Continuum
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中文总结 AI 辅助
本文提出Intent Engine架构，可将自然语言意图转换为计算连续体服务部署的经验证SLO制品，在多款大语言模型上均优于基线，能显著减少幻觉与部署失败率。
中文摘要 AI 辅助
计算连续体中的微服务部署由底层服务水平目标（SLO）驱动，但要求用户指定指标级约束会形成采用障碍并增加配置错误风险。尽管大语言模型（LLM）可解释自然语言意图，但直接生成编排可使用的SLO制品仍不可靠，原因包括不支持的约束、错误的基础值及架构违规，这些错误会传播至下游部署逻辑，产生不可行或错误的部署。本文提出Intent Engine，一种自然语言意图翻译架构，用于构建计算连续体服务部署的经验证SLO制品。Intent Engine作为现有意图驱动编排与部署框架的意图获取和SLO构建层，不执行部署或运行时QoS优化。该架构结合架构约束提取、基于监测基础设施状态的检索基础值构建，以及在输出最终SLO制品前对支持约束的验证。我们使用从边缘云测试床获取的含716条意图转SLO记录的数据集（含有效与无效意图）评估Intent Engine。在GPT-4.1 mini、Claude Sonnet 4.5和DeepSeek V4-Flash上，Intent Engine优于提示基线和非LLM基于规则的解析器。使用GPT-4.1 mini时，它达到0.941的总F1分数，减少85.1%的总体幻觉，同时将下游部署失败从30.8%降至2.1%。
英文摘要
Microservice placement in the compute continuum is driven by low-level Service-level Objectives (SLOs), but requiring users to specify metric-level constraints creates an adoption barrier and increases misconfiguration risk. Although large language models (LLMs) can interpret natural-language intents, direct generation of orchestration-consumable SLO artifacts remains unreliable due to unsupported constraints, incorrect grounded values, and schema violations. These errors can propagate to downstream placement logic and produce infeasible or incorrect placements. This paper presents Intent Engine, a natural-language intent translation architecture that constructs validated SLO artifacts for compute-continuum service placement. Intent Engine acts as an intent acquisition and SLO construction layer for existing intent-driven orchestration and placement frameworks; it does not perform placement or runtime QoS optimization. The architecture combines schema-constrained extraction, retrieval-grounded value construction from monitored infrastructure state, and validation against supported constraints before emitting the final SLO artifact. We evaluate Intent Engine using a 716-record intent-to-SLO dataset derived from an edge-cloud testbed, including valid and invalid intents. Across GPT-4.1 mini, Claude Sonnet 4.5, and DeepSeek V4-Flash, Intent Engine outperforms prompting baselines and a non-LLM rule-based parser. With GPT-4.1 mini, it achieves 0.941 total F1 Score and reduces aggregate hallucination by 85.1%, while lowering downstream placement failure from 30.8% to 2.1%.
发表机构
- Western Sydney University（西悉尼大学）
机构由 AI 辅助整理，请以论文原文为准。
