[2603.23580] MetaKube: An Experience-Aware LLM Framework for Kubernetes Failure Diagnosis

Computer Science > Machine Learning arXiv:2603.23580 (cs) [Submitted on 24 Mar 2026] Title:MetaKube: An Experience-Aware LLM Framework for Kubernetes Failure Diagnosis Authors:Wei Sun, Ting Wang, Xinran Tian, Wanshun Lan, Xuhan Feng, Haoyue Li, Fangxin Wang View a PDF of the paper titled MetaKube: An Experience-Aware LLM Framework for Kubernetes Failure Diagnosis, by Wei Sun and 6 other authors View PDF HTML (experimental) Abstract: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 con...