[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83668-en":3,"doc-seo-83668-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83668,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Operational Memory Architecture for Kubernetes Evidence Horizon Taxonomy and Extended Causal Pattern Preservation","Kubernetes clusters emit extensive operational events across pod lifecycle transitions, yet deterministic retention limits erase the most diagnostic context. The work defines an evidence horizon taxonomy with five unrecoverable boundaries, including loss of container failure forensics, scheduler placement rationale, ephemeral debug context, kubelet-level in-memory state after restarts, and observability blind spots for sub-scrape lifetimes. It extends the Operational Memory Architecture with new causal patterns, watchers, and SQLite tables, validating outcomes on Minikube and AKS through reproducible experiments.","arXiv :2607 .02528v1 [ cs .DC] 22 May 2026  \nKHAN: OPERATIONAL MEMORY ARCHITECTURE FOR KUBERNETES 1  \nOperational Memory Architecture for Kubernetes: Evidence Horizon Taxonomy and Extended Causal  \nPattern Preservation  \nShamsher Khan, Senior Member, IEEE Independent Researcher, Tampa Bay Area, FL, USA [shamsher.khan.research@gmail.com](shamsher.khan.research@gmail.com)  \nAbstract  \nKubernetes clusters generate rich operational events during pod lifecycle transitions, yet the platform’s native event retention model systematically discards the most diagnostically valuable context through multiple evidence destruction mechanisms operating on deterministic schedules. We formalize these mechanisms as an evidence horizon taxonomy: five distinct boundaries after which specific categories of diagnostic context become permanently unrecoverable from the Kubernetes API. H1 (LastTerminationState rotation, ∼90 s) destroys container failure forensics; H2 (scheduler event pruning, 1 hr/1000-event cluster limit) destroys placement rationale; H3 (ephemeral container exit, immediate) destroys debug session context due to an explicit exclusion in the API specification; H4 (kubelet reconciliation gap) destroys in-memory operational state across node restarts; and H5 (scrape-interval blind spot) renders sub-interval pod lifetimes structurally invisible to poll-based observability tools.  \nThis paper extends the Operational Memory Architecture (OMA), previously introduced for H1 causal pattern preservation, to address the full evidence horizon taxonomy. We define two new causal patterns: P004 (Scheduler Decision Provenance) captures FailedScheduling predicate failures and placement decisions before kube-apiserver TTL pruning, and demonstrates a novel cross-horizon causal chain (P004→P001) linking placement rationale to downstream OOMKill failures; P005 (Ephemeral Container Evidence Loss) captures EphemeralContainerStatus at the Terminated transition, providing the only mechanism that preserves exit code, session duration, and target container context after a kubectl debug session ends. H4 is analyzed theoretically as a kubelet-level integration boundary outside the current architecture. H5 is demonstrated empirically through comparative analysis: a pod with a 6-second lifetime—within one 15-second Prometheus scrape interval—generates zero time-series data in Prometheus while OMA captures the complete P001 causal chain at occurrence.  \nWe implement two new Go watchers (EventWatcher, EphemeralWatcher), extend the SQLite operational memory store with two new tables (scheduler_events, ephemeral_exits), and validate the extended architecture through reproducible experiments on Minikube (3-node, arm64) and Azure Kubernetes Service (AKS 1.32.10) . The original 30-run statistical latency analysis (242 edges, intra-cycle mean 0.702 ms, σ = 0 .31ms) and concurrent stress evaluation (2.86 events/sec at 20 pods, 8.8 MB RAM) are carried forward and augmented with H2, H3, and H5 empirical results. 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 through multiple evidence destruction mechanisms operating on deterministic schedules. We formalize these mechanisms as an evidence horizon taxonomy: five distinct boundaries after which specific categories of diagnostic context become permanently unrecoverable from the Kubernetes API. H1 (LastTerminationState rotation, ∼90 s) destroys container failure forensics; H2 (scheduler event pruning, 1 hr/1000-event cluster limit) destroys placement rationale; H3 (ephemeral container exit, immediate) destroys debug session context due to an explicit exclusion in the API specification; H4 (kubelet reconciliation gap) destroys in-memory operational state across node restarts; and H5 (scrape-interval blind spot) renders sub-interval pod lifetimes structurally invisible to poll-based","cbCairBfDfWiRIfs","https://ap.wps.com/l/cbCairBfDfWiRIfs","pdf",12461860,4,1,17,"English","en",105,"# Abstract\n## Evidence horizon taxonomy\n## Extended Operational Memory Architecture (OMA)\n## Implementation\n## Experimental validation and results","[{\"question\":\"What is the evidence horizon taxonomy in this paper?\",\"answer\":\"It formalizes five boundaries after which specific diagnostic context becomes permanently unrecoverable from the Kubernetes API, each tied to a distinct evidence destruction mechanism and timing schedule.\"},{\"question\":\"How does the paper extend OMA beyond the original H1 focus?\",\"answer\":\"It extends OMA to cover the full taxonomy by introducing new causal patterns, including Scheduler Decision Provenance (P004) and Ephemeral Container Evidence Loss (P005), and by addressing additional horizon behaviors through analysis and empirical demonstration.\"},{\"question\":\"How do the experiments demonstrate observability blind spots for short-lived pods?\",\"answer\":\"They compare Prometheus behavior for a pod with a 6-second lifetime against OMA capture, showing zero time-series data in Prometheus while OMA records the complete P001 causal chain at occurrence.\"}]",1784189636,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"operational-memory-architecture-for-kubernetes-evidence-horizon-taxonomy-and-extended-causal-pattern-preservation","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":20},"https://docshare.wps.com/document/operational-memory-architecture-for-kubernetes-evidence-horizon-taxonomy-and-extended-causal-pattern-preservation/83668/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-26","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the evidence horizon taxonomy in this paper?","Question",{"text":75,"@type":76},"It formalizes five boundaries after which specific diagnostic context becomes permanently unrecoverable from the Kubernetes API, each tied to a distinct evidence destruction mechanism and timing schedule.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper extend OMA beyond the original H1 focus?",{"text":80,"@type":76},"It extends OMA to cover the full taxonomy by introducing new causal patterns, including Scheduler Decision Provenance (P004) and Ephemeral Container Evidence Loss (P005), and by addressing additional horizon behaviors through analysis and empirical demonstration.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the experiments demonstrate observability blind spots for short-lived pods?",{"text":84,"@type":76},"They compare Prometheus behavior for a pod with a 6-second lifetime against OMA capture, showing zero time-series data in Prometheus while OMA records the 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