Live Architecture Models for Cloud-Native Architecture-as-Code: Early Results from Kubernetes Conformance Checking

📅 2026-09-28
📈 Citations: 0
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🤖 AI Summary
This study addresses the problem of Kubernetes infrastructure drift, where runtime states deviate from architectural intent, by proposing an editable living architecture model. This model explicitly maps runtime facts to architectural designs, supports bidirectional synchronization between textual and graphical views, and enables non-intrusive Architecture-as-Code management through periodic consistency checks. The proposed approach is implemented using a subset of the Archer KDL, a VS Code-based prototype tool, and snapshot restoration techniques. Experimental evaluations conducted on three representative applications validate the feasibility of the method, demonstrating strong performance in both snapshot restoration accuracy and inconsistency detection recall.
📝 Abstract
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.
Problem

Research questions and friction points this paper is trying to address.

Infrastructure drift
Kubernetes
Cloud-native architecture
Architecture-as-Code
Conformance checking
Innovation

Methods, ideas, or system contributions that make the work stand out.

Live Architecture Models
Kubernetes Deployment Language
Conformance Checking
Snapshot Recovery
Infrastructure Drift