Hybrid Resilience (H/R) Testing Model: AI-Driven Zero Downtime Deployment for Kubernetes

Hybrid Resilience (H/R) Testing Model: AI-Driven Zero Downtime Deployment for Kubernetes | 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 Hybrid Resilience (H/R) Testing Model: AI-Driven Zero Downtime Deployment for Kubernetes Haranath Rakshit, Subhasis Banerjee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6388101/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 Deploying applications in Kubernetes without any downtime is a big challenge. Current methods like Rolling Updates, Blue-Green, Canary, and A/B Testing wait until a failure happens before taking action. This can lead to unexpected disruptions, manual rollbacks, and slow recovery. To solve this, we introduce the Hybrid Resilience (H/R) Testing Model, a new way to predict and prevent failures before they happen. Our model combines AI-driven failure prediction, chaos engineering resilience testing, automated rollback using smart decision-making (MDP), and traffic-aware self-healing using Istio to ensure smooth and uninterrupted deployments. Instead of testing in a real-world setup, we focus on theoretical resilience metrics—Resilience Score (RS), Failure Probability (FP), and Chaos Impact (D)—to measure how strong a deployment is before it goes live. This model provides a guideline for future research, helping developers move from fixing failures after deployment to preventing them before deployment, making Kubernetes applications more reliable and self-healing. Kubernetes Resilience AI-Driven Failure Prediction Chaos Engineering Self-Healing Deployments Zero Downtime Automated Rollback Cloud-Native Systems Full Text Additional Declarations No competing interests reported. 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-6388101","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":439096281,"identity":"68e939d0-db60-4137-8bea-6c5358e638a4","order_by":0,"name":"Haranath Rakshit","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYBACAyidwMDAxsAM5MqBeAceENZiANNiYAzWkkC8FgaDxAaopTiBOfvZh58Lav7kmbcfS/5cUPAnfX7Y4YdAW+zkdBuwa7HsSTeWnnHMoFjmTNox6RkGBrkbb6cZALUkG5sdwOGwA2kM0jxsBokzGNLbmHlAWmYngLQcSNyGS8v5Z8y/ef4BtfA/b/4M1JJuODv9A34tN9LYpHnbgFok0g5IA7UkyEvnELDlxjM2a94+Y6CWZ2lALcaGG6RzCg4kGODxy/k05ts83+SADksz/szzR05efnb65g8fKuzkcGnBFiBgkljlICDfQIrqUTAKRsEoGAkAAOavXXQJi+0JAAAAAElFTkSuQmCC","orcid":"","institution":"Visva-Bharati","correspondingAuthor":true,"prefix":"","firstName":"Haranath","middleName":"","lastName":"Rakshit","suffix":""},{"id":439096282,"identity":"78c573d7-4ec8-4887-a6ed-555a08f2cfeb","order_by":1,"name":"Subhasis Banerjee","email":"","orcid":"","institution":"Visva-Bharati","correspondingAuthor":false,"prefix":"","firstName":"Subhasis","middleName":"","lastName":"Banerjee","suffix":""}],"badges":[],"createdAt":"2025-04-06 17:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6388101/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6388101/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108978218,"identity":"c1270adc-a4ce-4078-a1a8-c3b4ccfe53d9","added_by":"auto","created_at":"2026-05-11 11:35:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1947387,"visible":true,"origin":"","legend":"","description":"","filename":"HybridResilienceTestingModelAIDrivenZeroDowntimeDeploymentforKubernetesHR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6388101/v1_covered_74e398fe-7fb4-4e2a-8b2f-dc4a80e9ffd0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hybrid Resilience (H/R) Testing Model: AI-Driven Zero Downtime Deployment for Kubernetes","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"Kubernetes Resilience, AI-Driven Failure Prediction, Chaos Engineering, Self-Healing Deployments, Zero Downtime, Automated Rollback, Cloud-Native Systems","lastPublishedDoi":"10.21203/rs.3.rs-6388101/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6388101/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDeploying applications in Kubernetes without any downtime is a big challenge. 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