An Intelligent Green Controller for Dynamic Resource Provisioning in Heterogeneous Cloud–Edge IoT Systems

An Intelligent Green Controller for Dynamic Resource Provisioning in Heterogeneous Cloud–Edge IoT Systems | 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 An Intelligent Green Controller for Dynamic Resource Provisioning in Heterogeneous Cloud–Edge IoT Systems Kalpit Soni, Mubina Malik, Dhatri Raval, Unnati Patel, Atul Patel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8450394/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 The rapid growth of Internet of Things (IoT) applications has increased the demand for efficient, scalable, and energy-aware computing architectures capable of supporting latency-sensitive services. Traditional cloud-centric and edge-only models often suffer from high latency, resource underutilization, and energy inefficiencies in dynamic environments. To address these challenges, this paper proposes an intelligent energy-aware edge–cloud collaborative framework that jointly optimizes energy consumption, task latency, and resource utilization. The proposed framework integrates adaptive workload offloading, multi-objective scheduling, and real-time feedback control to dynamically allocate tasks across heterogeneous edge and cloud resources. A mathematical energy consumption model is developed to quantify computation and communication overheads, enabling informed scheduling decisions. The framework is evaluated using extensive simulations on CloudSim and iFogSim under diverse workload and network conditions. Experimental results show that the proposed approach consistently outperforms cloud-only, edge-only, and single-objective scheduling strategies across multiple performance metrics. It achieves up to 28% reduction in total energy consumption, 35% improvement in task latency, and improved load balancing and Quality of Service (QoS), demonstrating its effectiveness for scalable and energy-efficient edge–cloud computing environments. Edge–Cloud Computing Energy-Efficient Scheduling Internet of Things (IoT) Multi-Objective Optimization Sustainable Computing 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. 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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-8450394","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":570538123,"identity":"cab47a86-572d-4042-adc3-aca16d553b18","order_by":0,"name":"Kalpit Soni","email":"data:image/png;base64,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","orcid":"","institution":"Charotar University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Kalpit","middleName":"","lastName":"Soni","suffix":""},{"id":570538124,"identity":"15963f5d-f945-48b0-baeb-d9a7f8cfcb33","order_by":1,"name":"Mubina Malik","email":"","orcid":"","institution":"Charotar University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Mubina","middleName":"","lastName":"Malik","suffix":""},{"id":570538125,"identity":"85124394-665a-4c5a-801a-859e68a793d1","order_by":2,"name":"Dhatri Raval","email":"","orcid":"","institution":"Charotar University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Dhatri","middleName":"","lastName":"Raval","suffix":""},{"id":570538126,"identity":"1ce74f0f-2593-4f1e-9c49-d3c33236ceeb","order_by":3,"name":"Unnati Patel","email":"","orcid":"","institution":"Charotar University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Unnati","middleName":"","lastName":"Patel","suffix":""},{"id":570538127,"identity":"c733f36c-dc0f-428a-b8cf-f88e9d3a336b","order_by":4,"name":"Atul Patel","email":"","orcid":"","institution":"Charotar University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Atul","middleName":"","lastName":"Patel","suffix":""}],"badges":[],"createdAt":"2025-12-25 17:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8450394/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8450394/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99798306,"identity":"0760e888-c624-4055-a69c-37a6c099d12a","added_by":"auto","created_at":"2026-01-08 13:47:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":507178,"visible":true,"origin":"","legend":"","description":"","filename":"RP101225.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8450394/v1_covered_dac60150-e9ee-4ac1-bada-42c13a355a02.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Intelligent Green Controller for Dynamic Resource Provisioning in Heterogeneous Cloud–Edge IoT Systems","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":"Edge–Cloud Computing, Energy-Efficient Scheduling, Internet of Things (IoT), Multi-Objective Optimization, Sustainable Computing","lastPublishedDoi":"10.21203/rs.3.rs-8450394/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8450394/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid growth of Internet of Things (IoT) applications has increased the demand for efficient, scalable, and energy-aware computing architectures capable of supporting latency-sensitive services. Traditional cloud-centric and edge-only models often suffer from high latency, resource underutilization, and energy inefficiencies in dynamic environments. To address these challenges, this paper proposes an intelligent energy-aware edge\u0026ndash;cloud collaborative framework that jointly optimizes energy consumption, task latency, and resource utilization.\u003c/p\u003e \u003cp\u003eThe proposed framework integrates adaptive workload offloading, multi-objective scheduling, and real-time feedback control to dynamically allocate tasks across heterogeneous edge and cloud resources. A mathematical energy consumption model is developed to quantify computation and communication overheads, enabling informed scheduling decisions. 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