Comprehensive enhancements for machine-learning based cloud resource orchestration algorithms

Comprehensive enhancements for machine-learning based cloud resource orchestration algorithms | 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 Article Comprehensive enhancements for machine-learning based cloud resource orchestration algorithms István Pintye, József Kovács, Róbert Lovas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4491313/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 With the increasing utilisation of machine-learning algorithms, autoscaling methods have the potential to provide more sophisticated control mechanisms for the cloud application operators over virtualized resources in terms of provisioning. This paper introduces machine learning-based autoscaling approach introducing several improvements including metric selection, proactivity, various enhancements of the neural network among others. Our enhanced machine learning models enables the autoscaler algorithm to react more quickly to sudden load changes, increase proactivity while using fewer resources and reducing Quality of Service (QoS) violations for cloud-based services. The comprehensive measurements indicate that QoS violations may reduce by up to 80%, while the level of resource utilization either remains constant or decreases slightly (by 3-4%) in certain applications. In cases where there was no reduction in QoS violations, the utilization of resources saw a significant decline, falling between 20-50%. The proposed changes have been analyzed and tested under various conditions, representing 3 distinct and common use cases in cloud environments. This developed process has been implemented on the science cloud of the Hungarian Research Network supporting the operation of the infrastucture that hosts over 300 scientific projects. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Computational science cloud computing orchestration autoscaling machine learning metric selection resource 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-4491313","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":312645086,"identity":"49334bf4-4246-46e2-b4d8-cf9f18e21129","order_by":0,"name":"István Pintye","email":"data:image/png;base64,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","orcid":"","institution":"Institute for Computer Science and Control (HUN-REN SZTAKI)","correspondingAuthor":true,"prefix":"","firstName":"István","middleName":"","lastName":"Pintye","suffix":""},{"id":312645087,"identity":"4160eca6-8df3-4e25-ba7f-cbf3a297fefb","order_by":1,"name":"József Kovács","email":"","orcid":"","institution":"Institute for Computer Science and Control (HUN-REN SZTAKI)","correspondingAuthor":false,"prefix":"","firstName":"József","middleName":"","lastName":"Kovács","suffix":""},{"id":312645088,"identity":"e2e21842-5518-4bf7-98a2-37bc6f66cdf1","order_by":2,"name":"Róbert Lovas","email":"","orcid":"","institution":"Institute for Computer Science and Control (HUN-REN SZTAKI)","correspondingAuthor":false,"prefix":"","firstName":"Róbert","middleName":"","lastName":"Lovas","suffix":""}],"badges":[],"createdAt":"2024-05-28 13:36:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4491313/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4491313/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58375261,"identity":"b2d8139c-e8e6-4c84-9530-345675f38b3f","added_by":"auto","created_at":"2024-06-14 15:02:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5376723,"visible":true,"origin":"","legend":"","description":"","filename":"JOGCsubmitteComprehensivenhancement20240529.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4491313/v1_covered_f83c420b-422e-47d1-b7d2-8cb04220e1e4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive enhancements for machine-learning based cloud resource orchestration algorithms","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":"cloud computing, orchestration, autoscaling, machine learning, metric selection, resource optimization","lastPublishedDoi":"10.21203/rs.3.rs-4491313/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4491313/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"With the increasing utilisation of machine-learning algorithms, autoscaling methods have the potential to provide more sophisticated control mechanisms for the cloud application operators over virtualized resources in terms of provisioning. 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