Energy-Aware DVFS-Driven Workload Provisioning in Heterogeneous Cloud FaaS Architectures

Energy-Aware DVFS-Driven Workload Provisioning in Heterogeneous Cloud FaaS Architectures | 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 Energy-Aware DVFS-Driven Workload Provisioning in Heterogeneous Cloud FaaS Architectures Lucas Rister Machado, Gregory Moraes Rossato, Antonio Carlos Schneider Beck, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6727085/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Cloud Warehouses are evolving with diverse computational resources, including CPUs, GPUs, and accelerators, catering to a multitude of tenant applications. While this heterogeneity promises improved performance and energy effciency, harnessing its full potential poses challenges due to dynamic workload characteristics and variable application demands. To address this, scheduling approaches combined with optimization techniques like Dynamic Voltage and Frequency Scaling (DVFS) are crucial. However, integrating these approaches effectively can be complex, potentially leading to conflicts and diminished benefits. This research proposes two frameworks, EAPECloud and EAPECloud-DVFS, designed for energy-aware collaborative provisioning in heterogeneous CPU-GPU cloud nodes. The first approach reduces energy consumption by selecting and maintaining a static combination of the best scheduler and V-F pair for most workloads. The second approach goes further by dynamically adjusting the V-F pair of each device using DVFS techniques while selecting the optimal scheduler. While the static approach delivers strong results in most cases, the dynamic strategy achieves even greater energy savings, albeit with an additional convergence time to determine the optimal V-F pair. Although each framework has distinct advantages and use cases, our findings demonstrate that both approaches effectively reduce energy consumption in heterogeneous environments, with EAPECloud-DVFS achieving up to a 126.33% performance improvement compared to the Linux CPU Governor, highlighting its effciency and applicability in real-time systems. Cloud Computing Function as a Service Provisioning DVFS Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Oct, 2025 Reviews received at journal 26 Oct, 2025 Reviews received at journal 24 Oct, 2025 Reviews received at journal 19 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviewers invited by journal 07 Oct, 2025 Editor assigned by journal 27 May, 2025 Submission checks completed at journal 27 May, 2025 First submitted to journal 22 May, 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. 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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-6727085","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531267182,"identity":"3842fa8f-4588-43ae-a58a-8bff7d4bdb6d","order_by":0,"name":"Lucas Rister Machado","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYBACPijNAyY/wIQrgNgAhxY2uBYgi3EGTPgMEVrALGYeorRIJD/8+IPhnoz8/OZjn20q7OR0ZzewSRz4s43BXPoADi1pxtI8DMU8BsfYkmfnnEk2NrtzgE3iYNttBsu+BBxachikGRgSeAzYeIyZc9sOJG67kcAm/bHhNoPBGVwOy2H++QOoRb6N/zOz5T+IFqDD8Gphk+ABamE4xsPMzNgA08KGRwvPMzNrHgOgw46lGTP2HAP55WCzBdAvPJY92LXwsyc/vvmjIsFevvnwY4YfNXZyZrebD94AOkzOnAe7FghAiQIJxgYQhVcDGpAgQe0oGAWjYBSMCAAAmLJUSKWpGDYAAAAASUVORK5CYII=","orcid":"","institution":"Universidade Federal de Santa Maria – UFSM","correspondingAuthor":true,"prefix":"","firstName":"Lucas","middleName":"Rister","lastName":"Machado","suffix":""},{"id":531267183,"identity":"2869a7b0-2774-4fac-af6a-c3c2f88851ee","order_by":1,"name":"Gregory Moraes Rossato","email":"","orcid":"","institution":"Universidade Federal de Santa Maria – UFSM","correspondingAuthor":false,"prefix":"","firstName":"Gregory","middleName":"Moraes","lastName":"Rossato","suffix":""},{"id":531267184,"identity":"e51563fe-1138-4312-849e-850cda7ffd1c","order_by":2,"name":"Antonio Carlos Schneider Beck","email":"","orcid":"","institution":"Universidade Federal do Rio Grande do Sul – UFRGS","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"Carlos Schneider","lastName":"Beck","suffix":""},{"id":531267185,"identity":"8e50c7a6-f1d8-44ab-bf70-61a943d26e67","order_by":3,"name":"Michael G. 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