Operational Memory Architecture for Kubernetes Preserving Causal Context Across the Evidence Horizon

Jul 16, 2026

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Kubernetes clusters emit operational events during pod lifecycle transitions, yet native retention and state-rotation semantics discard the diagnostic context needed for reliable incident response. The LastTerminationState field is overwritten within ~90 seconds after a pod restart, defining the evidence horizon, within which forensic reconstruction remains possible. Operational Memory Architecture (OMA) encodes evidence-rotation constraints and causal preservation as first-class requirements and captures three causal patterns (P001–P003). OMA is implemented with a Go watcher, SQLite store, and three-query interface, validated on Minikube and AKS.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

Abstract

Index Terms

Introduction

Problem: Ephemeral event retention and evidence horizon

Motivation: Diagnosing crash loops under constraints

Related work: Limits of existing observability

arXiv :2605 . 18755v1 [ cs .DC] 6 Mar 2026 KHAN: OPERATIONAL MEMORY ARCHITECTURE FOR KUBERNETES Operational Memory Architecture for Kubernetes: Preserving Causal Context Across the Evidence Horizon Shamsher Khan, Senior Member, IEEE GlobalLogic (Hitachi Group), Tampa Bay Area, FL, USA [shamsher.khan.research@gmail.com](shamsher.khan.research@gmail.com) 1 Abstract Kubernetes clusters generate rich operational events during pod lifecycle transitions, yet the platform’s native event retention model systematically discards the most diagnostically valuable context. The LastTerminationState field, which records the reason and exit code of a container’s most recent failure, is overwritten within approximately 90 seconds of a pod restart—a boundary we term the evidence horizon, characterized experimentally in Section V. During high-frequency crash loops, this horizon can be crossed multiple times before an engineer reaches a terminal, permanently destroying forensic evidence. Kubernetes clusters generate rich operational events during pod lifecycle transitions, yet the platform’s native event retention model systematically discards the most diagnostically valuable context. The LastTerminationState field, which records the reason and exit code of a container’s most recent failure, is overwritten within approximately 90 seconds of a pod restart—a boundary we term the evidence horizon, characterized experimentally in Section V. During high-frequency crash loops, this horizon can be crossed multiple times before an engineer reaches a terminal, permanently destroying forensic evidence. This paper presents the Operational Memory Architecture (OMA), formalized as a first-class architectural primitive for Kubernetes operational state preservation. OMA explicitly encodes evidence rotation constraints and causal preservation as design requirements, an aspect not addressed by existing observability or logging models. OMA introduces three encoded causal patterns: P001 (OOMKill causal chain), P002 (ConfigMap environment variable silent misconfiguration), and P003 (ConfigMap volume mount symlink swap propagation) . We implement OMA as an open-source system comprising a Go-based Kubernetes watcher, a SQLite operational memory store, and a canonical three-query interface. We validate the architecture through reproducible experiments on Minikube and Azure Kubernetes Service (AKS 1.32.10), and conduct a 30-run statistical latency analysis and concurrent stress evaluation with up to 20 simultaneous crash-looping pods. Intra-cycle causal edges are constructed with a mean latency of 0.702 ms (σ = 0 .31ms) . The collector processes 2.86 events/sec under 20 concurrent OOMKill pods while consuming only 8.8 MB RAM, confirming linear scaling and minimal operational overhead. While validated on Minikube and Azure Kubernetes Service, OMA addresses a fundamental property of the Kubernetes architecture itself: the evidence horizon arises from the kubelet’s state rotation semantics, which are consistent across all conformant Kubernetes distributions and managed services. Index Terms AKS, causal inference, cloud-native, container orchestration, evidence horizon, incident response, Kubernetes, observability, operational memory, site reliability engineering I. INTRODUCTION Modern cloud-native applications run as collections of containerized microservices orchestrated by Kubernetes. As these deployments grow in scale and complexity, the operational challenge of diagnosing failures has become a significant engineering bottleneck. When a container crashes, the immediate question The implementation, experimental data, and evaluation scripts described in this paper are publicly available at [https://github.com/opscart/k8s](https://github.com/opscart/k8s)causal-memory. e-mail: [shamsher.khan.research@gmail.com](shamsher.khan.research@gmail.com)) Raw event logs, SQLite databases, query outputs, and automation scripts for all experiments are committe

FAQs

What problem does the evidence horizon describe in Kubernetes diagnostics?

It is the short window during which post-mortem investigation remains possible before LastTerminationState and related direct failure evidence are overwritten by pod restart behavior.

How does OMA preserve causal context across the evidence horizon?

OMA encodes evidence-rotation constraints and causal preservation requirements, explicitly capturing encoded causal patterns from Kubernetes operational state changes.

What components and validation setup are used to implement and evaluate OMA?

OMA is implemented with a Go-based Kubernetes watcher and a SQLite operational memory store, exposed via a canonical three-query interface, and validated through reproducible experiments on Minikube and Azure Kubernetes Service (AKS).

Jul 16, 2026

Upload by Jiven