A Convolutional Neural Networks Stochastic Petri Nets Hybrid Modeling Approach for Reliable Classification and Monitoring of Hadoop Cluster Nodes

A Convolutional Neural Networks Stochastic Petri Nets Hybrid Modeling Approach for Reliable Classification and Monitoring of Hadoop Cluster Nodes | 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 A Convolutional Neural Networks Stochastic Petri Nets Hybrid Modeling Approach for Reliable Classification and Monitoring of Hadoop Cluster Nodes Walid Ben Mesmia, Zied Trifa, Kamel Barkaoui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8344421/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Ensuring the reliability and security of large-scale distributed infrastructures such as Hadoop clusters requires monitoring models capable of capturing both deterministic structural behavior and stochastic variations related to workload dynamics and potential anomalies. To address these challenges, we introduce a novel CNN-SPN hybrid model that explicitly combines a Convolutional Neural Network (CNN) with a Stochastic Petri Net (SPN) incorporating multiple probability distributions (multi-density), including exponential, normal, log-normal, Poisson, and Weibull laws. The SPN component provides a rigorous formalization of system behavior, while the CNN component learns discriminative patterns enabling supervised classification of nodes into stable and non-stable operational states. The hybrid architecture leverages the strengths of both paradigms: stochastic modeling to capture temporal and probabilistic transitions, and deep learning to generalize from observed execution traces. The proposed model is evaluated through simulation using the TimeNet environment, complemented by real cluster activity logs. Experimental results demonstrate that the CNN-SPN multi-density approach significantly improves classification performance, reaching an accuracy of 97.8%, a precision of 96.4%, a recall of 95.7%, and an F1-score of 96.0%, confirming consistency among metrics. Compared to traditional models such as Logistic Regression, SVM, and Random Forest, the CNN-SPN hybrid not only achieves higher accuracy but also effectively captures rare or extreme node failures that conventional models may miss. These results confirm the efficiency of integrating multi-density stochastic representation with deep neural architectures for detecting abnormal behaviors and enhancing the overall security and resilience of distributed systems. Convolutional Neural Networks Stochastic Petri Nets Classification Hadoop Nodes Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 Feb, 2026 Reviewers invited by journal 12 Feb, 2026 Editor invited by journal 07 Feb, 2026 First submitted to journal 11 Dec, 2025 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-8344421","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590006681,"identity":"baf57a30-ed01-45e0-9b5e-a744e76245e9","order_by":0,"name":"Walid Ben Mesmia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIie3SMUvDQBTA8VcK6XIljq9Y9CtcKVwoCH6Vk0KyZHAKnTRTXFpc029Rl863iEvofOUNjYtThkzSIaAXdfSs3QTvD7lAyI+7Bwfgcv3B+NebmaejpPd2ew/gtV/83xAwpNtZpp9kkB4gHykwZKUOkKC3eFzVzXro59NSlYnXHevpS1nPAAOLmcw34XaZEUMdciU3zBM6DEZ5AThUloPpWFA/JQYkubrKkAktxWk/gxu0zbKrBDUNsXOKakM4jvPotSVoJZoJAo8Yp7jdRXKOsfiRTOax2C7MLKNddW1mURKLKhnkBVpJ0CuE3jd0eVY8PTzvEyX9u2iN9ezCSr7pRLbrEcDcFHXM3y6Xy/UPegct42JdemRCNAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-1270-5916","institution":"ENIT: Ecole Nationale d'Ingenieurs de Tunis","correspondingAuthor":true,"prefix":"","firstName":"Walid","middleName":"Ben","lastName":"Mesmia","suffix":""},{"id":590006682,"identity":"954c8f05-21f3-4534-9e13-e903575afd4c","order_by":1,"name":"Zied Trifa","email":"","orcid":"","institution":"MIRACL Research Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Zied","middleName":"","lastName":"Trifa","suffix":""},{"id":590006683,"identity":"58ecfac6-fdf1-46fd-adfe-7e1bbd8b0e48","order_by":2,"name":"Kamel Barkaoui","email":"","orcid":"","institution":"CNAM: Conservatoire National des Arts et Metiers","correspondingAuthor":false,"prefix":"","firstName":"Kamel","middleName":"","lastName":"Barkaoui","suffix":""}],"badges":[],"createdAt":"2025-12-12 10:00:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8344421/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8344421/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102964671,"identity":"4b28dd30-92c4-4888-afeb-c307ece0b6b1","added_by":"auto","created_at":"2026-02-19 04:23:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":619974,"visible":true,"origin":"","legend":"","description":"","filename":"snarticle12122025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8344421/v1_covered_9d90ab87-ec5f-4bb8-ae0e-6f013005dba8.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eA Convolutional Neural Networks Stochastic Petri Nets Hybrid Modeling Approach for Reliable Classification and Monitoring of Hadoop Cluster Nodes\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Convolutional Neural Networks, Stochastic Petri Nets, Classification, Hadoop Nodes","lastPublishedDoi":"10.21203/rs.3.rs-8344421/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8344421/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnsuring the reliability and security of large-scale distributed infrastructures such as Hadoop clusters requires monitoring models capable of capturing both deterministic structural behavior and stochastic variations related to workload dynamics and potential anomalies. To address these challenges, we introduce a novel CNN-SPN hybrid model that explicitly combines a Convolutional Neural Network (CNN) with a Stochastic Petri Net (SPN) incorporating multiple probability distributions (multi-density), including exponential, normal, log-normal, Poisson, and Weibull laws. The SPN component provides a rigorous formalization of system behavior, while the CNN component learns discriminative patterns enabling supervised classification of nodes into stable and non-stable operational states. The hybrid architecture leverages the strengths of both paradigms: stochastic modeling to capture temporal and probabilistic transitions, and deep learning to generalize from observed execution traces.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe proposed model is evaluated through simulation using the TimeNet environment, complemented by real cluster activity logs. 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