Building and Validating Deep Learning Models for Forecasting the Quality of Cloud Services

Building and Validating Deep Learning Models for Forecasting the Quality of Cloud Services | 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 Building and Validating Deep Learning Models for Forecasting the Quality of Cloud Services Ximena Guerron, Silvia Abrahao, Marta Fernandez-Diego, Emilio Insfran, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7829797/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Context: Cloud services operate in highly dynamic and heterogeneous environments, requiring a continuous and accurate assessment of service quality. While Quality of Service (QoS) models are widely used to monitor performance, deep learning (DL) architectures—such as Long Short-Term Memory (LSTM) and Bidirectional Gated Recurrent Units (BI-GRU)—offer enhanced capabilities for forecasting potential SLA violations. However, many existing experiments in this domain suffer from methodological shortcomings, including the use of outdated or proprietary datasets, a narrow set of QoS metrics, incomplete documentation of model architectures and training procedures, and a lack of statistical rigor, all of which undermine reproducibility and applicability in industrial contexts. Objective: This study empirically compares the performance of BI-GRU, LSTM, and AutoRegressive Integrated Moving Average (ARIMA) models for QoS forecasting, using a rigorously designed experimental protocol that addresses these limitations. Method : We developed a multi-metric QoS dataset covering five months of operational data from a cloud service in an IT company, comprising 16 QoS metrics. Forecasting models were trained and evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with training time as an efficiency indicator. Results: BI-GRU outperformed ARIMA across all QoS metrics models and outperformed LSTM in 10 out of 16 QoS forecasting models (62.5%) with competitive or shorter training times. Conclusion: Our findings demonstrate that BI-GRU models deliver superior accuracy and efficiency and that methodological rigor supports their applicability for proactive QoS management and informed decision-making in industrial cloud service environments. Cloud Services QoS Deep Learning Time-series Modeling BI-GRU Empirical Study Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 31 Jan, 2026 Reviews received at journal 26 Jan, 2026 Reviews received at journal 20 Jan, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviews received at journal 23 Dec, 2025 Reviewers agreed at journal 01 Dec, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers invited by journal 14 Nov, 2025 Editor assigned by journal 14 Oct, 2025 Submission checks completed at journal 14 Oct, 2025 First submitted to journal 10 Oct, 2025 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-7829797","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":550070089,"identity":"749d6352-5c95-483c-bcab-144684243ec6","order_by":0,"name":"Ximena 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Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cloud Services, QoS, Deep Learning, Time-series Modeling, BI-GRU, Empirical Study","lastPublishedDoi":"10.21203/rs.3.rs-7829797/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7829797/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Context: Cloud services operate in highly dynamic and heterogeneous environments, requiring a continuous and accurate assessment of service quality. While Quality of Service (QoS) models are widely used to monitor performance, deep learning (DL) architectures—such as Long Short-Term Memory (LSTM) and Bidirectional Gated Recurrent Units (BI-GRU)—offer enhanced capabilities for forecasting potential SLA violations. However, many existing experiments in this domain suffer from methodological shortcomings, including the use of outdated or proprietary datasets, a narrow set of QoS metrics, incomplete documentation of model architectures and training procedures, and a lack of statistical rigor, all of which undermine reproducibility and applicability in industrial contexts. Objective: This study empirically compares the performance of BI-GRU, LSTM, and AutoRegressive Integrated Moving Average (ARIMA) models for QoS forecasting, using a rigorously designed experimental protocol that addresses these limitations. 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