Improved Binary Whale Optimization-based Resource Allocation in Edge Computing Environment

Improved Binary Whale Optimization-based Resource Allocation in Edge Computing Environment | 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 Improved Binary Whale Optimization-based Resource Allocation in Edge Computing Environment Meng Li, Ting Lyu, Fuhong Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7717330/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 an edge computing environment, a large number of interconnected smart devices can provide effective resources to solve the contradiction between the supply and demand of computing. How to make full use of the diversified and flexible idle resource supply with scattered hardware locations to improve the task execution efficiency and overall support capability has become a great challenge. Thus, we investigated the optimal allocation scheme of edge computing resources with the objective of system delay optimization. First of all, we established a time-minimizing resource allocation model for the edge computing environment, with the decision method of selecting pairs among nodes and satisfying the device energy constraint. Secondly, we proposed a heuristic algorithm, namely Improved Binary Whale Optimization Algorithm (IBWOA), to solve the problem and demonstrated the effectiveness of the heuristic algorithm in a small-scale case. Finally, the performance evaluation and comparative analysis demonstrated that the proposed algorithm outperforms the Random Assignment (RAN) algorithm and Binary Particle Swarm Optimization (BPSO) algorithm in terms of final results, convergence speed, and stability. edge computing resource allocation heuristic algorithm binary whale optimization 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-7717330","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":539620390,"identity":"326bd30f-1472-4431-876d-c3106736d112","order_by":0,"name":"Meng Li","email":"","orcid":"","institution":"University of Science and Technology Beijing","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Li","suffix":""},{"id":539620391,"identity":"94077419-7b19-427c-b241-90167eb7cf93","order_by":1,"name":"Ting 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Environment","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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 computing, resource allocation, heuristic algorithm, binary whale optimization","lastPublishedDoi":"10.21203/rs.3.rs-7717330/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7717330/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In an edge computing environment, a large number of interconnected smart devices can provide effective resources to solve the contradiction between the supply and demand of computing. How to make full use of the diversified and flexible idle resource supply with scattered hardware locations to improve the task execution efficiency and overall support capability has become a great challenge. Thus, we investigated the optimal allocation scheme of edge computing resources with the objective of system delay optimization. First of all, we established a time-minimizing resource allocation model for the edge computing environment, with the decision method of selecting pairs among nodes and satisfying the device energy constraint. Secondly, we proposed a heuristic algorithm, namely Improved Binary Whale Optimization Algorithm (IBWOA), to solve the problem and demonstrated the effectiveness of the heuristic algorithm in a small-scale case. Finally, the performance evaluation and comparative analysis demonstrated that the proposed algorithm outperforms the Random Assignment (RAN) algorithm and Binary Particle Swarm Optimization (BPSO) algorithm in terms of final results, convergence speed, and stability.","manuscriptTitle":"Improved Binary Whale Optimization-based Resource Allocation in Edge Computing Environment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 16:40:00","doi":"10.21203/rs.3.rs-7717330/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"5dfd42aa-e5c2-48d4-9d97-7a7d6c4facff","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T00:24:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 16:40:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7717330","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7717330","identity":"rs-7717330","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}