CloudPulse: A Computational Intelligence Method for Energy-Efficient Data Center Steering UsingMulti-Armed Bandits

CloudPulse: A Computational Intelligence Method for Energy-Efficient Data Center Steering UsingMulti-Armed Bandits | 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 CloudPulse: A Computational Intelligence Method for Energy-Efficient Data Center Steering UsingMulti-Armed Bandits Angelina George, Challagulla Sai Bhumika, Potula Radha Nishant, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7276451/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 Modern data centers, built around interconnected server clusters, underpin large-scale cloud computing but face challenges like resource heterogeneity, workload imbalance, and energy consumption. To address these, this research introduces CloudPulse, a smart scheduling framework integrating Reinforcement Learning (RL) and Federated Learning (FL) to optimize workload distribution and task migration in real time. CloudPulse monitors power states, operational requirements, and constraints to manage server utilization and costs, with FL enabling decentralized training to reduce data transfer energy, and RL enhancing adaptive decisions to minimize environmental impact. It also addresses consumer over-or underestimation of resources by suggesting sustainable allocations that align demand with workload needs. The RL approach refines scheduling even for low-power or resource-intensive workloads, improving efficiency and performance. CloudPulse features a Streamlit app for real-time monitoring with secure ngrok access. Experiments show it outperforms traditional approaches by placing work-loads holistically across racks and servers with an FL model to maximize median or trimmed mean efficiency, minimizing inefficiency through computational steering that allocates cores per task based on effectiveness measures such as 1/(T/NC) and 1/(T/NC·NS), balancing server load and prolonging resource life without frequent data center changes. Cluster Computing High Performance Computing Energy Efficiency Energy Conservation Federated Reinforcement Learning Green Cloud Computing 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. 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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-7276451","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514934148,"identity":"fdf550e1-027a-4a02-b66c-6cb05904e25b","order_by":0,"name":"Angelina George","email":"","orcid":"","institution":"Amrita Vishwa Vidyapeetham","correspondingAuthor":false,"prefix":"","firstName":"Angelina","middleName":"","lastName":"George","suffix":""},{"id":514934149,"identity":"c4d9a863-1eec-4989-b740-8b9c0ba980a8","order_by":1,"name":"Challagulla Sai Bhumika","email":"","orcid":"","institution":"Amrita Vishwa 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