Entropy-Regularized Uncertainty-Aware Multi-Objective Optimization for Serverless Data Pipelines using Hybrid Reinforcement Learning

Entropy-Regularized Uncertainty-Aware Multi-Objective Optimization for Serverless Data Pipelines using Hybrid Reinforcement Learning | 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 Entropy-Regularized Uncertainty-Aware Multi-Objective Optimization for Serverless Data Pipelines using Hybrid Reinforcement Learning Venkat Alamuri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9382021/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 Serverless data pipelines have a very dynamic, non-stationary workload, bursty request patterns, cold-start delays, and resource variability are the order of the day, and traditional one-dimensional or statical optimization strategies do not apply. The currently used methods mainly aim at minimizing either cost or latency without paying much attention to predictive uncertainty and its effects on the stability of the decision making which results in suboptimal and unstable scheduling in real world situations. In a bid to fill this gap, this paper introduces a new multi-objective framework called ERUO (Entropy-Regularized Uncertainty-Aware Optimization) that concurrently optimizes the latency, cost, probability of cold-start, and uncertainty in a single reinforcement learning framework. ERUO proposes to use an entropy-based regularization on predictive uncertainty distributions to allow the system to discourage unstable regimes of decisions and enhance resistance to workload variability. The structure combines the use of quantile-based uncertainty modelling, gradient-boosted prediction, LSTM-based workload forecasting and PPO-based policy optimizer. As shown by experimental assessment on real-world cloud traces (Alibaba and Google cluster datasets), ERUO can make latency, cost, and cold-start instances reductions of up to 23.6, 18.4 and 27.1 over baseline approaches, as well as converge faster and can make improved decisions. The importance of this work is that uncertainty entropy has been formally coupled with the control optimization, which offers an efficient and scalable solution to managing next-generation serverless data pipelines. Artificial Intelligence and Machine Learning Computer Architecture and Engineering Serverless Computing Multi-Objective Optimization Reinforcement Learning Uncertainty Modelling Cloud Computing Entropy Regularization Workload Prediction Resource Allocation Cold Start Optimization Distributed Systems 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. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9382021","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621128262,"identity":"dcc21cb7-abf6-4758-a5b7-70a392ca0f8e","order_by":0,"name":"Venkat Alamuri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYHACZjDJByYNbIAEY+MBwloSGBjYIFrSQFoaSNHCcBhM4tVicLz3sMHPHwzybOyn06QrCs7brW0/DLSlxiYap5Yz55ITexIYDNt4crdJnjG4nbztTCJQy7G03AYcWiRn5Bgf4ElgYGxjAGppAGoxOwDUwthwGLeW+W+MD/5JYLBv438L0nIu2ez8Q/xa+CV4jJOBtiS2SYBtOWBndoOALfw8OcbGMmkSyW0SbzdbNhgkJ5jdANqSgMcvbOxnjCXf2NjY9vPnbrzZ8MfO3ux8+sMHH2pscGqBAgk4KxGsMgG/clRgT4riUTAKRsEoGBkAAOSkW9N+oNlrAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0000-7460-047X","institution":"NO","correspondingAuthor":true,"prefix":"","firstName":"Venkat","middleName":"","lastName":"Alamuri","suffix":""}],"badges":[],"createdAt":"2026-04-10 17:16:02","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-9382021/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9382021/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106846646,"identity":"7d973a27-229f-4583-8638-ccbe86538906","added_by":"auto","created_at":"2026-04-14 05:10:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4986180,"visible":true,"origin":"","legend":"","description":"","filename":"Entropy.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9382021/v1_covered_91a80d72-488e-4b21-af33-cada257cc084.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEntropy-Regularized Uncertainty-Aware Multi-Objective Optimization for Serverless Data Pipelines using Hybrid Reinforcement Learning\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"OTHERS","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":"Serverless Computing, Multi-Objective Optimization, Reinforcement Learning, Uncertainty Modelling, Cloud Computing, Entropy Regularization, Workload Prediction, Resource Allocation, Cold Start Optimization, Distributed Systems","lastPublishedDoi":"10.21203/rs.3.rs-9382021/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9382021/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eServerless data pipelines have a very dynamic, non-stationary workload, bursty request patterns, cold-start delays, and resource variability are the order of the day, and traditional one-dimensional or statical optimization strategies do not apply. 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