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This work aims to enhance the Operational Memory Architecture (OMA) framework by developing a comprehensive taxonomy of evidence horizons to better manage causal context in Kubernetes.

April 28, 2026Open Access

Operational Memory Architecture for Kubernetes: Evidence Horizon Taxonomy and Extended Causal Pattern Preservation

Key Points

This work aims to enhance the Operational Memory Architecture (OMA) framework by developing a comprehensive taxonomy of evidence horizons to better manage causal context in Kubernetes.
Expanded the OMA framework from one evidence horizon to five (H1–H5).
Introduced additional causal pattern horizons and comparative analysis among them.
Validated implementations on Minikube and AKS 1.32.10.
Developed a full taxonomy of five causal horizons improving diagnostic context preservation.
Enhanced Go collector implementation and scenario directories for storage migrations.

Abstract

This work extends our prior publication (DOI: 10.5281/zenodo.19685352) by expanding the Operational Memory Architecture (OMA) framework from a single evidence horizon (H1) to a full taxonomy of five horizons (H1–H5). OMA is an open-source framework for preserving causal diagnostic context in Kubernetes clusters before it is destroyed by the platform's native event retention mechanisms. The extended framework introduces four additional causal pattern horizons, a comparative analysis across horizon types, and enriched Go collector implementation with additional scenario directories and storage migrations. Validated on Minikube and AKS 1.32.10. Full implementation at https://github.com/opscart/k8s-causal-memory

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