让大模型学会从运维经验中持续学习，提升K8s故障诊断准确率。
MetaKube: An Experience-Aware LLM Framework for Kubernetes Failure Diagnosis
- 构建记忆网络抽象历史故障模式，支持精准匹配与因果探索。
- 引入自适应控制器，根据问题熟悉度自动切换快速或深入分析路径。
- 基于7000条真实故障数据微调80亿参数模型，诊断准确率提升至90.5分。
现有基于大模型的Kubernetes诊断系统无法从运维经验中学习，仅依赖静态知识库且无法迭代优化。本文提出MetaKube，一个具备经验感知能力的大模型框架，包含三项协同创新：(1) 事件模式记忆网络（EPMN），从历史故障解决记录中抽象诊断模式，提供置信度校准的检索，支持快速匹配与引导式因果分析；(2) 元认知控制器，根据问题熟悉度动态切换直觉与分析路径，平衡诊断速度与深度；(3) KubeLLM，一个在7000样本的Kubernetes故障修复数据集上进行领域特定后训练的本地可部署80亿参数模型。在1873个真实场景下的评估显示，MetaKube将Qwen3-8B的性能从50.9分提升至90.5分，接近GPT-4.1水平，同时保障数据隐私。EPMN通过经验学习贡献15.3%的性能提升，连续学习实验表明系统随运维知识积累持续进步。代码与资源已开源。
原文摘要 · Abstract (English)
Existing LLM-based Kubernetes diagnostic systems cannot learn from operational experience, operating on static knowledge bases without improving from past resolutions. We present MetaKube, an experience-aware LLM framework through three synergistic innovations: (1) an Episodic Pattern Memory Network (EPMN) that abstracts diagnostic patterns from historical resolutions and provides confidence-calibrated retrieval for both rapid pattern matching and guided causal exploration, (2) a meta-cognitive controller that dynamically routes between intuitive and analytical pathways based on problem familiarity, optimizing the trade-off between speed and depth, and (3) KubeLLM, a locally-deployable 8B model enhanced through domain-specific post-training on our 7,000-sample Kubernetes Fault Resolution Dataset. Evaluation on 1,873 real-world scenarios demonstrates MetaKube transforms Qwen3-8B from 50.9 to 90.5 points, approaching GPT-4.1 performance while ensuring complete data privacy. EPMN contributes 15.3% improvement through experiential learning, with continuous learning experiments showing progressive gains as the system accumulates operational knowledge. The source code and related resources are available at https://github.com/MetaKube-LLM-for-Kubernetes-Diagnosis/MetaKube.
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