GRU-OptiCom: Revolutionizing Computation Offloading in Edge Computing through Meta-Reinforcement Learning with GRU

GRU-OptiCom: Revolutionizing Computation Offloading in Edge Computing through Meta-Reinforcement Learning with GRU | 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 GRU-OptiCom: Revolutionizing Computation Offloading in Edge Computing through Meta-Reinforcement Learning with GRU Aditya Oza, Yash Vardhan Gautam, Anirudh Bhakar, Dr. Mallikharjuna Rao K This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5694969/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 In the current scenario, mobile devices face the problem of lack of computational power which limits their capability of data processing. To solve this problem, the Multi-access Edge Computing (MEC) framework is an optimal solution that enables the mobile device to offload the heavy tasks to the connected edge servers. This offloading not only reduces the computational burden on mobile devices but also helps in reducing latency making the application real-time. The MEC framework is specifically designed to optimize the offloading process using advanced optimization techniques. To manage the computational burden, these strategies are essential in redistributing jobs between mobile devices and edge servers. To assess the performance of the framework we use a metric called Latency. Also we have done the comparative analysis between this framework and other known techniques like MRLCO , HEFT-based greedy algorithms etc. This comparison shows the superiority of the MEC framework in reducing latency and providing optimal load distribution. This research provides a novel approach to task offloading and also sets a new benchmark for future advancements in the field of mobile and edge computing. This research provides a novel MEC framework which acts as a cornerstone in the advancement of mobile computing technologies. Multi-access edge computing HEFT-based Greedy algorithms computational load task offloading 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. 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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-5694969","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":428374027,"identity":"8db59400-03c1-4c70-b6c5-73e8773d8c36","order_by":0,"name":"Aditya Oza","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYFCCxAYgYcPDxsx88EGCgQREkAeMcGppBOpJk+Fjb0s2AGqRIEJLAiNQy2EbOZ4zZiDlcC04AX97cvtjnhpmHjaJBLOKBwUWdebSBxgfvG1jkDHHoUXizMPGZp5jbCAtaTdADrPsS2A2nNvGwGPZgEPPjUSgFjYekJZjYC0GZxjYpHmBWgwOYNchD9byTwKoJbGtAKqF/Tc+LQYgLbxtBkCLDrMxwGxhxqfFEOiXmXP7EnjY2NuYJYBaJDecYWyWnHNOAqcWuePpDz68+fbfXr6Z/+PHH3/q+A3OMB/88KbMxh6XFhBgQosFUERB4wcXYPyBV3oUjIJRMApGPAAAePNTwlgzMpEAAAAASUVORK5CYII=","orcid":"","institution":"International Institute of Information Technology Naya Raipur","correspondingAuthor":true,"prefix":"","firstName":"Aditya","middleName":"","lastName":"Oza","suffix":""},{"id":428374028,"identity":"27689e6c-e3d0-4f1f-9458-f64e619de450","order_by":1,"name":"Yash Vardhan Gautam","email":"","orcid":"","institution":"International Institute of Information Technology Naya Raipur","correspondingAuthor":false,"prefix":"","firstName":"Yash","middleName":"Vardhan","lastName":"Gautam","suffix":""},{"id":428374029,"identity":"87e0bad9-df5d-4809-a6bb-c0559bbd82f5","order_by":2,"name":"Anirudh Bhakar","email":"","orcid":"","institution":"International Institute of Information Technology Naya Raipur","correspondingAuthor":false,"prefix":"","firstName":"Anirudh","middleName":"","lastName":"Bhakar","suffix":""},{"id":428374030,"identity":"3764f69f-d136-43c5-a724-587a05ee1086","order_by":3,"name":"Dr. Mallikharjuna Rao K","email":"","orcid":"","institution":"International Institute of Information Technology Naya Raipur","correspondingAuthor":false,"prefix":"Dr.","firstName":"Mallikharjuna","middleName":"Rao","lastName":"K","suffix":""}],"badges":[],"createdAt":"2024-12-22 17:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5694969/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5694969/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78628519,"identity":"1835dd76-b79d-4f7a-87d4-d4004e555c6b","added_by":"auto","created_at":"2025-03-17 03:00:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":625264,"visible":true,"origin":"","legend":"","description":"","filename":"GRUOPTFinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5694969/v1_covered_9fdd83d4-691a-44ff-a33f-2e777127d63f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eGRU-OptiCom: Revolutionizing Computation Offloading in Edge Computing through Meta-Reinforcement Learning with GRU\u003c/p\u003e","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":"Multi-access edge computing, HEFT-based Greedy algorithms, computational load, task offloading","lastPublishedDoi":"10.21203/rs.3.rs-5694969/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5694969/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the current scenario, mobile devices face the problem of lack of computational power which limits their capability of data processing. 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