发表机构
Moscow Institute of Physics and Technology; Kangwon National University（莫斯科物理技术学院; 江原国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对Kubernetes基础设施漂移问题，提出基于KDL子集和Archer原型的实时架构模型，通过快照恢复与只读一致性检查，在三个示例应用中验证了可行性，但未证明比较优势与扩展性。
AI 中文摘要
基础设施漂移可能使运行中的Kubernetes系统与其文档化的架构意图相分离。本文研究了实时架构模型：通过显式对应关系和周期性一致性检查，将可编辑的架构表示与选定的运行时事实相连接。该方法通过Archer特定的Kubernetes部署语言（KDL）子集和Archer（一个具有同步文本和图形视图的VS Code原型）进行实例化。快照恢复将选定的Kubernetes事实提取到KDL中；周期性和按需的只读检查报告模型-集群不一致性，而不强制或修复部署状态。我们在作者定义的协议下，对三个功能选择的Kubernetes示例应用评估了可行性，报告了快照恢复和选定不一致性检测的精确率和召回率。恢复分数可从保存的基准事实和恢复模型中复现；检测使用文档化的手动扰动协议。结果支持在所评估的KDL范围内的可行性。它们并未确立比较优势、任意生产漂移的检测、可扩展性或开发者收益。存储覆盖和入口主机表示/默认处理仍然是局限性。
英文摘要
Infrastructure drift can separate a running Kubernetes system from its documented architectural intent. This paper investigates live architecture models: editable architectural representations connected to selected runtime facts through explicit correspondences and recurring conformance checks. The approach is instantiated through an Archer-specific subset of Kubernetes Deployment Language (KDL) and Archer, a VS Code prototype with synchronized textual and graphical views. Snapshot recovery extracts selected Kubernetes facts into KDL; periodic and on-demand read-only checks report model-cluster inconsistencies without enforcing or repairing deployment state. We assess feasibility on three feature-selected Kubernetes example applications under an author-defined protocol, reporting precision and recall for snapshot recovery and selected inconsistency detection. Recovery scores can be reproduced from saved ground-truth and recovered models; detection uses a documented manual perturbation protocol. The results support feasibility within the evaluated KDL scope. They do not establish comparative superiority, detection of arbitrary production drift, scalability, or developer benefit. Storage coverage and ingress-host representation/default handling remain limitations.
Comments16 pages, 5 figures, 9 tables. Code and evaluation artifacts: https://github.com/AsakoKabe/archer-kdl
