Adaptive Real-Time Big Data Processing Framework: A Machine Learning and Reinforcement Learning Approach Using Random Forest and Q-Learning for Dynamic Resource Management

Adaptive Real-Time Big Data Processing Framework: A Machine Learning and Reinforcement Learning Approach Using Random Forest and Q-Learning for Dynamic Resource Management | 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 Adaptive Real-Time Big Data Processing Framework: A Machine Learning and Reinforcement Learning Approach Using Random Forest and Q-Learning for Dynamic Resource Management Akash Hooda, Arju Hooda, Disha Yadav This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4962286/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Real-time big data analytics is crucial for industries that handle high-velocity data streams, such as IoT, finance, e-commerce, and social media. Traditional big data processing frameworks often struggle to adapt to the dynamic nature of real-time data, leading to inefficiencies, increased latency, and higher operational costs. This research proposes an Adaptive Real-Time Big Data Processing Framework that integrates a Random Forest model for load prediction with Q-learning for dynamic resource allocation. The framework adjusts in real-time based on predictive insights and learned policies, optimizing system performance while minimizing costs. Experimental results demonstrate that the framework reduces latency by 15%, improves throughput by 20%, compared to traditional methods. The Random Forest model achieved an efficiency of 93.62%, while the Q-learning approach further achieved 94.86%, reflecting superior resource utilization. These improvements indicate the framework's effectiveness in dynamically adapting to real-time data, ensuring high performance and cost-efficiency. Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-4962286","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":347353197,"identity":"bcb4371c-39dc-494a-b96e-4c02be9a2e9b","order_by":0,"name":"Akash Hooda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYDACHgYGZsYGIOMAY8OBhAogg5m5gVgtzI0HHpxhgHGJ0sLefPBhG0iIgBb+nsOPPxfusJHjO36w4UDivNpo/naglh8V23BqkTjbZiY980yaseSZRKCWbcdzZxxmbGDsOXMbtzXnGcyYedsOJ244ANZyLLcBqIWZsQ23Fvnz7J8/g7WcfwjUMudY7nxCWgzO9hhIg7XcANnSUJO7gZAWwzNnyoBagH65AbQl4diB3I1ALQfx+UXuTPpmoMOAIXY+/fHHHzV1ufPOHz744EcFHu+jgcNg8gDR6oGgjhTFo2AUjIJRMEIAALrEaoP+kb15AAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Akash","middleName":"","lastName":"Hooda","suffix":""},{"id":347353198,"identity":"de763ffa-4d68-4ccc-a52d-d3007aad352c","order_by":1,"name":"Arju Hooda","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Arju","middleName":"","lastName":"Hooda","suffix":""},{"id":347353199,"identity":"ee6d5ba4-d1dd-4260-bb98-6868a5f2fd5e","order_by":2,"name":"Disha Yadav","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Disha","middleName":"","lastName":"Yadav","suffix":""}],"badges":[],"createdAt":"2024-08-23 07:23:44","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4962286/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-4962286/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64870234,"identity":"82c4231e-9a23-46a4-8f5c-484b28b42f74","added_by":"auto","created_at":"2024-09-19 21:01:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":521023,"visible":true,"origin":"","legend":"","description":"","filename":"Dynamicresourceallocation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4962286/v2_covered_516659e3-95fe-4853-8ad9-54d3f24061af.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Adaptive Real-Time Big Data Processing Framework: A Machine Learning and Reinforcement Learning Approach Using Random Forest and Q-Learning for Dynamic Resource Management","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":"","lastPublishedDoi":"10.21203/rs.3.rs-4962286/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4962286/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReal-time big data analytics is crucial for industries that handle high-velocity data streams, such as IoT, finance, e-commerce, and social media. 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