Energy-efficient offloading framework for mobile edge/cloud computing based on Convex Optimization and Deep Q-Network

Energy-efficient offloading framework for mobile edge/cloud computing based on Convex Optimization and Deep Q-Network | 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 Energy-efficient offloading framework for mobile edge/cloud computing based on Convex Optimization and Deep Q-Network Askar Madiyev, Daulet Bulegenov, Anuar Karzhaubayev, Meiram Murzabulatov, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6263061/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 29 You are reading this latest preprint version Abstract Energy efficiency is one of the most critical aspects of modern computing paradigms due to minimizing carbon footprint and lowering operational costs. To achieve efficiency, the typical approach is to address the source of energy consumption and apply the appropriate strategies for energy savings. In this paper, based on an offloading framework for edge and cloud computing, we proposed a comprehensive methodology that leverages predictive analysis and convex optimization techniques to achieve efficiency in power utilization. This methodology aimed to reduce the power consumption of edge/cloud computing clusters while maintaining an acceptable quality of service. The core idea was to enhance the historical data in the first place by using the prediction. This predictive historical data revealed the trend of computational resource allocation. Subsequently, the convex optimization technique coupled with the Deep Q-Network (DQN) model was employed to formulate and schedule the distribution of the offloaded tasks. By engaging this combination, the offloading framework could produce a near-optimal and adaptive energy decision, which helps achieve energy efficiency. The experimental results showed that the proposed methodology could obtain significant energy savings while maintaining a suitable level of performance compared to other state-of-the-art approaches. cloud computing computation offloading convex optimization energy efficiency mobile edge computing reinforcement learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Apr, 2025 Reviews received at journal 06 Apr, 2025 Reviews received at journal 03 Apr, 2025 Reviewers agreed at journal 02 Apr, 2025 Reviews received at journal 02 Apr, 2025 Reviewers agreed at journal 31 Mar, 2025 Reviews received at journal 31 Mar, 2025 Reviewers agreed at journal 30 Mar, 2025 Reviews received at journal 29 Mar, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers agreed at journal 26 Mar, 2025 Reviewers agreed at journal 25 Mar, 2025 Reviewers agreed at journal 25 Mar, 2025 Reviewers agreed at journal 25 Mar, 2025 Reviewers agreed at journal 25 Mar, 2025 Reviewers agreed at journal 25 Mar, 2025 Reviewers agreed at journal 24 Mar, 2025 Reviewers agreed at journal 24 Mar, 2025 Reviewers agreed at journal 24 Mar, 2025 Reviewers agreed at journal 24 Mar, 2025 Reviewers agreed at journal 24 Mar, 2025 Reviewers invited by journal 24 Mar, 2025 Editor assigned by journal 24 Mar, 2025 Submission checks completed at journal 23 Mar, 2025 First submitted to journal 19 Mar, 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. 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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-6263061","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":437703215,"identity":"1a4009bd-0d4d-4244-b0fd-685e8b0ef5ab","order_by":0,"name":"Askar Madiyev","email":"","orcid":"","institution":"Nazarbayev University","correspondingAuthor":false,"prefix":"","firstName":"Askar","middleName":"","lastName":"Madiyev","suffix":""},{"id":437703216,"identity":"6757b51f-a702-4175-bf8c-6887b803005e","order_by":1,"name":"Daulet Bulegenov","email":"","orcid":"","institution":"Nazarbayev University","correspondingAuthor":false,"prefix":"","firstName":"Daulet","middleName":"","lastName":"Bulegenov","suffix":""},{"id":437703217,"identity":"a98ebdb3-9524-4bfc-8aff-bf418eb9bbc6","order_by":2,"name":"Anuar Karzhaubayev","email":"","orcid":"","institution":"Nazarbayev University","correspondingAuthor":false,"prefix":"","firstName":"Anuar","middleName":"","lastName":"Karzhaubayev","suffix":""},{"id":437703218,"identity":"bf391802-b342-4732-9c72-0de3efde6ed6","order_by":3,"name":"Meiram Murzabulatov","email":"","orcid":"","institution":"Nazarbayev University","correspondingAuthor":false,"prefix":"","firstName":"Meiram","middleName":"","lastName":"Murzabulatov","suffix":""},{"id":437703219,"identity":"5c0ae3ec-4b5c-42e4-a650-0a9f192ec866","order_by":4,"name":"Dinh Mao Bui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAm0lEQVRIiWNgGAWjYDCCAzyMDxJI1cJsQLIWNgnSdPBdO3us4kGFjRwDe+/jF0Rpkbydl3Yj4UyaMQPPcTMLorQY3M4xu5HYdjixQSKNzYBoLQWJ//7Xk6aFIbHhQAKDRBrzA6K0AP2SLJFwLNmwjecYG1E6GPhu5x78+KPGTp6fvY35A3F6YABoBakRBASk2jIKRsEoGAUjBQAAxeQucrszt9sAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Dinh","middleName":"Mao","lastName":"Bui","suffix":""}],"badges":[],"createdAt":"2025-03-19 15:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6263061/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6263061/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79816652,"identity":"0f705c52-2396-4030-8b57-a902493acb06","added_by":"auto","created_at":"2025-04-03 07:53:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1736961,"visible":true,"origin":"","legend":"","description":"","filename":"EEDQNSupercomputing2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6263061/v1_covered_2611fed5-b41d-4b78-b03b-acd219e70c3b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Energy-efficient offloading framework for mobile edge/cloud computing based on Convex Optimization and Deep Q-Network","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"cloud computing, computation offloading, convex optimization, energy efficiency, mobile edge computing, reinforcement learning","lastPublishedDoi":"10.21203/rs.3.rs-6263061/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6263061/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Energy efficiency is one of the most critical aspects of modern computing paradigms due to minimizing carbon footprint and lowering operational costs. 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