Parallel Hybrid Metaheuristic and Branch-and-Bound Scheduler

Parallel Hybrid Metaheuristic and Branch-and-Bound Scheduler | 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Article Parallel Hybrid Metaheuristic and Branch-and-Bound Scheduler Youcef BENMOUNA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7159765/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 Task scheduling in cloud computing is a critical optimization challenge due to resource heterogeneity and dynamic workloads. Metaheuristic algorithms, such as the Flower Pollination Algorithm (FPA), provide fast and near-optimal solutions but may lack precision in complex scenarios. After a comparative evaluation on the GoCJ dataset, FPA was selected for its favorable trade-off between solution quality and execution time. This work introduces HMBB-Sched, a hybrid algorithm that combines FPA with the exact Branch and Bound (B&B) method to enhance solution quality and convergence. To further improve scalability, we propose Parallel_HMBB-Sched, which processes task clusters in parallel using multithreading. All experiments were conducted using CloudSim and the GoCJ dataset, ensuring consistency and reliability of the evaluation. Results show that the hybrid method significantly reduces makespan and execution time compared to standalone FPA. The multithreaded version achieves considerable speedups, particularly under high workloads. This study highlights the effectiveness of combining hybridization and parallelism for efficient and scalable task scheduling in cloud environments. Cloud Computing Task Scheduling FPA B&B Hybridization Multithreading 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. 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-7159765","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":487689722,"identity":"4ec4309a-6806-42e5-96a9-c020e8ae6ec7","order_by":0,"name":"Youcef BENMOUNA","email":"data:image/png;base64,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","orcid":"","institution":"Tlemcen University","correspondingAuthor":true,"prefix":"","firstName":"Youcef","middleName":"","lastName":"BENMOUNA","suffix":""}],"badges":[],"createdAt":"2025-07-18 17:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7159765/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7159765/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90923295,"identity":"f610409f-7700-42c7-ab21-5ce6a3e8c4f0","added_by":"auto","created_at":"2025-09-09 15:17:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":550027,"visible":true,"origin":"","legend":"","description":"","filename":"HMBBBENM.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7159765/v1_covered_97638f0c-a25b-4619-8575-50687c1076d5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Parallel Hybrid Metaheuristic and Branch-and-Bound Scheduler","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":"Cloud Computing, Task Scheduling, FPA, B\u0026B, Hybridization, Multithreading","lastPublishedDoi":"10.21203/rs.3.rs-7159765/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7159765/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTask scheduling in cloud computing is a critical optimization challenge due to resource heterogeneity and dynamic workloads. Metaheuristic algorithms, such as the Flower Pollination Algorithm (FPA), provide fast and near-optimal solutions but may lack precision in complex scenarios. After a comparative evaluation on the GoCJ dataset, FPA was selected for its favorable trade-off between solution quality and execution time. This work introduces HMBB-Sched, a hybrid algorithm that combines FPA with the exact Branch and Bound (B\u0026amp;B) method to enhance solution quality and convergence.\u003c/p\u003e\u003cp\u003eTo further improve scalability, we propose Parallel_HMBB-Sched, which processes task clusters in parallel using multithreading. All experiments were conducted using CloudSim and the GoCJ dataset, ensuring consistency and reliability of the evaluation. Results show that the hybrid method significantly reduces makespan and execution time compared to standalone FPA. The multithreaded version achieves considerable speedups, particularly under high workloads. 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