Adaptive Kafka Resource Allocation for Real-Time Monitoring in the Manufacturing Sector

Adaptive Kafka Resource Allocation for Real-Time Monitoring in the Manufacturing Sector | 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 Adaptive Kafka Resource Allocation for Real-Time Monitoring in the Manufacturing Sector Anindya Lokeswara, Bryan 'Ilman, Naufal Widyatama This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7044326/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 The deployment of Industry 4.0 technologies in the manufacturing sector creates an immense demand for scalable and efficient real-time data processing. This paper presents an adaptive resource allocation framework to address these challenges. We introduce a methodology for creating and managing adaptive Kafka clusters, encompassing broker, partition, and consumer scaling. The framework's core for dynamic resource provisioning is an algorithm named Bromin, supplemented by KEDA for consumer autoscaling. We first establish the importance of empirical, hardware-specific parameterization, showing that our tuned configuration consistently meets throughput target where configurations based on industry-standard estimates fail. We then evaluate the fully adaptive cluster under various loads. While throughput remained high, the study revealed critical limitations in consumer scaling under extreme load, leading to a complete halt in message processing. Finally, we demonstrate the framework's practical application in an end-to-end real-time monitoring and alerting system. Real-Time Systems Apache Kafka Autoscaling Throughput Fog Computing Industry 4.0 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. 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-7044326","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":505688425,"identity":"158d89aa-79b9-4457-adb9-2355ba125c1c","order_by":0,"name":"Anindya Lokeswara","email":"","orcid":"","institution":"University of Indonesia","correspondingAuthor":false,"prefix":"","firstName":"Anindya","middleName":"","lastName":"Lokeswara","suffix":""},{"id":505688426,"identity":"5f4bf681-ba9a-4485-8ff7-a7f1d62caa40","order_by":1,"name":"Bryan 'Ilman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYBACAxiDTQJIfGCQAHMO8BCrhXEGSVpAipnhKvFpMZc+Y/i5gOGePJ9087PHtm0WDPztBxgPvMGjxbIvx1h6BkOxYZvMMXPj3DYJBokzCQwH5+Bz2BkeA2kehgTGNokEM2mQFoYbDAyH8frlDI/xb6AW+zaJ9G/SlkAt8kRoMQPZktgmkWMmDbSLwYCQFssetjJrHoOEZKCWMsmecxI8hmcSG/D6xZyHefNtnooE2/kz0rdJ/Cirk5M7fvjwB3whxsDAYYAcO6AYYWzAq4GBgf0BAQWjYBSMglEw4gEAkWlAVfW/WuIAAAAASUVORK5CYII=","orcid":"","institution":"University of Indonesia","correspondingAuthor":true,"prefix":"","firstName":"Bryan","middleName":"","lastName":"'Ilman","suffix":""},{"id":505688427,"identity":"1239ac3d-1ff2-4b33-b39d-a70970c55190","order_by":2,"name":"Naufal Widyatama","email":"","orcid":"","institution":"University of Indonesia","correspondingAuthor":false,"prefix":"","firstName":"Naufal","middleName":"","lastName":"Widyatama","suffix":""}],"badges":[],"createdAt":"2025-07-04 07:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7044326/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7044326/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98624331,"identity":"1ec1fbee-e26b-4a54-88d1-98db57c96a49","added_by":"auto","created_at":"2025-12-19 17:08:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1475298,"visible":true,"origin":"","legend":"","description":"","filename":"adaptivekafkaforindustryrealtimesystems.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7044326/v1_covered_1ea5a4bb-7fd6-49b3-85fd-a4b81665aa11.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adaptive Kafka Resource Allocation for Real-Time Monitoring in the Manufacturing Sector","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Real-Time Systems, Apache Kafka, Autoscaling, Throughput, Fog Computing, Industry 4.0","lastPublishedDoi":"10.21203/rs.3.rs-7044326/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7044326/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe deployment of Industry 4.0 technologies in the manufacturing sector creates an immense demand for scalable and efficient real-time data processing. This paper presents an adaptive resource allocation framework to address these challenges. We introduce a methodology for creating and managing adaptive Kafka clusters, encompassing broker, partition, and consumer scaling. The framework's core for dynamic resource provisioning is an algorithm named Bromin, supplemented by KEDA for consumer autoscaling. We first establish the importance of empirical, hardware-specific parameterization, showing that our tuned configuration consistently meets throughput target where configurations based on industry-standard estimates fail. We then evaluate the fully adaptive cluster under various loads. While throughput remained high, the study revealed critical limitations in consumer scaling under extreme load, leading to a complete halt in message processing. Finally, we demonstrate the framework's practical application in an end-to-end real-time monitoring and alerting system.\u003c/p\u003e","manuscriptTitle":"Adaptive Kafka Resource Allocation for Real-Time Monitoring in the Manufacturing Sector","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-28 05:33:04","doi":"10.21203/rs.3.rs-7044326/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"e71d01dc-c6cd-4ad5-91c0-db11d09f3526","owner":[],"postedDate":"August 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-18T00:38:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-28 05:33:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7044326","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7044326","identity":"rs-7044326","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}