REACTOR: Reliability Engineering with Automated Causal Tracking and Observability Reasoning

REACTOR: Reliability Engineering with Automated Causal Tracking and Observability Reasoning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article REACTOR: Reliability Engineering with Automated Causal Tracking and Observability Reasoning Bingxin Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6866975/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Reliability engineering aims to ensure that systems perform as expected over time, yet it faces various challenges in identifying and mitigating potential failures. We introduce REACTOR, an advanced framework prioritizing automated causal tracking and observability reasoning to improve reliability analysis. REACTOR uniquely utilizes a dual-layer architecture to facilitate the identification of failure sources through thorough causal analysis and subsequently assesses the ramifications of these failures on system performance through observability reasoning. This framework minimizes reliance on manual interventions, enabling users to achieve a deeper understanding of the reliability of complex systems. We employ sophisticated machine learning techniques to bolster the detection of anomalies and pinpoint their root causes, fostering a proactive approach to reliability management. Computer Architecture and Engineering Causal Tracking Reasoning Model Reliability Engineering Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6866975","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469506576,"identity":"c3cf5b68-8c57-4ca7-884c-a9094eb7781d","order_by":0,"name":"Bingxin Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACA2YwySAH5j0AEQeI1GIM5iUQpQVKJzYQrcWcnffwa56CO+nzHXgMPyTUMMjx3UjAr8WymS/NcobBs9yNB3iMJRKOMRhLEtJicJjHzOCDweHcjQ08ZgwJbAyJG4jSkmBwON0QrOUfQz0xWowfAG1JkGcAaklsY0gwIOwXHjPGGQaHDTcwsxVLJPZJGM488wC/FnP+M8afef4clpdvb9744cM3G3m+4wRsAQI2CYgLwRwJgspBgPkDiJRvIErxKBgFo2AUjEQAAOoeQa+6DJ8LAAAAAElFTkSuQmCC","orcid":"","institution":"University of California, Los Angeles","correspondingAuthor":true,"prefix":"","firstName":"Bingxin","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2025-06-11 02:05:18","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6866975/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6866975/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84434954,"identity":"8436221b-e0a0-455d-a1a8-ed659791eaf5","added_by":"auto","created_at":"2025-06-12 02:01:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":585282,"visible":true,"origin":"","legend":"","description":"","filename":"REACTORReliabilityEngineeringwithAutomatedCausalTrackingandObservabilityReasoning3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6866975/v1_covered_f8e655a2-bf77-49fc-ac67-72a825cdfb3d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eREACTOR: Reliability Engineering with Automated Causal Tracking and Observability Reasoning\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"UCLA Medical Center","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Causal Tracking, Reasoning Model, Reliability Engineering","lastPublishedDoi":"10.21203/rs.3.rs-6866975/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6866975/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReliability engineering aims to ensure that systems perform as expected over time, yet it faces various challenges in identifying and mitigating potential failures. 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