Deep Neural Architecture Combining Frequency and Attention Mechanisms for Cloud CPU Usage Prediction

Deep Neural Architecture Combining Frequency and Attention Mechanisms for Cloud CPU Usage Prediction | 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 Deep Neural Architecture Combining Frequency and Attention Mechanisms for Cloud CPU Usage Prediction Ming Wang, Sibo Wang, Yilin Li, Ziyu Cheng, Song Han This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7661765/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 This study addresses the key problem of CPU utilization prediction in cloud computing environments and proposes a time series modeling method based on an improved FedFormer. The research first analyzes the complexity of cloud workloads under high concurrency and dynamic fluctuations, and points out the limitations of traditional methods in handling nonlinear, non-stationary, and noisy data with insufficient accuracy and weak robustness. To address this issue, the proposed approach introduces an improved structure that integrates frequency-domain modeling with attention mechanisms. Global periodic patterns are extracted through the fast Fourier transform, while multi-head self-attention captures both local and long-range dependencies, enabling efficient modeling of CPU utilization sequences. A unified feature embedding and prediction framework is constructed, where raw time series data are transformed into high-dimensional representations at the input layer, and nonlinear mappings generate predictions in the output stage, optimized by the mean squared error loss function. In experimental design, the study conducts multidimensional validation, including hyperparameter sensitivity, environment sensitivity, and data sensitivity. The results show that the method maintains stable predictive performance in complex cloud computing scenarios and achieves higher accuracy and robustness than multiple baseline models. The findings enrich the methodological system of deep time series modeling in cloud computing and provide solid technical support for resource scheduling, load balancing, and performance optimization. Cloud computing CPU usage prediction frequency domain modeling timing dependency deep learning Full Text Additional Declarations The authors declare no competing interests. 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-7661765","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":517876528,"identity":"08716cd4-8894-49dc-b102-f194c7f42c04","order_by":0,"name":"Ming Wang","email":"","orcid":"","institution":"Trine University","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Wang","suffix":""},{"id":517876529,"identity":"518a1b69-727f-40f8-a54a-341ac9a815e4","order_by":1,"name":"Sibo Wang","email":"","orcid":"","institution":"Rice University","correspondingAuthor":false,"prefix":"","firstName":"Sibo","middleName":"","lastName":"Wang","suffix":""},{"id":517876530,"identity":"1aef1f39-8397-4ed1-8e88-7a8294e39aeb","order_by":2,"name":"Yilin Li","email":"","orcid":"","institution":"Carnegie Mellon University","correspondingAuthor":false,"prefix":"","firstName":"Yilin","middleName":"","lastName":"Li","suffix":""},{"id":517876531,"identity":"2a418c25-4eaa-4275-a34a-575a5d1c84e6","order_by":3,"name":"Ziyu Cheng","email":"","orcid":"","institution":"University of Southern California","correspondingAuthor":false,"prefix":"","firstName":"Ziyu","middleName":"","lastName":"Cheng","suffix":""},{"id":517876532,"identity":"2548ca79-935f-42dd-8cb3-3786a7c3f3c1","order_by":4,"name":"Song Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYLACxgYQmcDA8AFIsbGTooVxBkgLMylamHlANCEt8u29h18w7rDLY2BPPiZt82ubPB8zA+OHjzm4tRicOZdmwXgmuZiB51madG7fbcM2ZgZmyZnb8GiRyDEzYGxjTmyQyDE2zu25DWQDvcOLR4v8DLCWeqCW/M/Glj237QlqYbiRY/yAse0wyBbGxww/bicS1GJw5owZQ2Lb8cQ2nmeGD3sbbie3MTM24/WLfHuP8YePbdWJ/ezJDw78+HPbdn5788EPH/E5DBh3EgkgEsRkbAOTDXjVAwHzBwT7DyHFo2AUjIJRMBIBABIwTkOd7dIsAAAAAElFTkSuQmCC","orcid":"","institution":"Northeastern University","correspondingAuthor":true,"prefix":"","firstName":"Song","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2025-09-20 02:15:01","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7661765/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7661765/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91942263,"identity":"5f2f9086-7757-4143-83ee-0d479d83280b","added_by":"auto","created_at":"2025-09-23 04:22:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":820849,"visible":true,"origin":"","legend":"","description":"","filename":"DeepNeuralArchitectureCombiningFrequencyandAttentionMechanismsforCloudCPUUsagePrediction.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7661765/v1_covered_7856aa22-683d-4a0b-828c-b0a27adcba55.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eDeep Neural Architecture Combining Frequency and Attention Mechanisms for Cloud CPU Usage Prediction\u003c/p\u003e","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, CPU usage prediction, frequency domain modeling, timing dependency, deep learning","lastPublishedDoi":"10.21203/rs.3.rs-7661765/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7661765/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study addresses the key problem of CPU utilization prediction in cloud computing environments and proposes a time series modeling method based on an improved FedFormer. 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