{"paper_id":"2b25fc68-6c0c-41c5-8f4b-af01f0a00b50","body_text":"Automated Deployment and Performance Benchmarking of Machine Learning Workloads on Hadoop Clusters Using Ansible | 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 Automated Deployment and Performance Benchmarking of Machine Learning Workloads on Hadoop Clusters Using Ansible Rameez Rahaman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7003490/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 With the growing adoption of big data frameworks in cloud computing, ensuring efficient deployment and performance evaluation of distributed systems has become crucial. This project focuses on automating the deployment of machine learning benchmarks on Hadoop clusters using Ansible, a leading DevOps tool for IT automation. We leverage HiBench, a comprehensive benchmarking suite developed by Intel, to evaluate the runtime and throughput of Naïve Bayes Classification and K-Means Clustering workloads implemented in Apache Mahout. The deployment process involves setting up a virtual cluster, installing necessary software packages, and configuring Hadoop and Spark environments through Ansible playbooks. The study presents benchmarking results across different workload scales, analyzing execution time and throughput per node. By automating the entire setup, this work simplifies the evaluation of large-scale machine learning tasks, making it easier to assess and optimize Hadoop-based distributed computing environments. Ansible Automation Hadoop Cluster Deployment Machine Learning Benchmarking Cloud Infrastructure Automation Apache Mahout 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. 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