ADD-QIA: An Adaptive Data Deduplication Framework Based on Quantum Immune Algorithm

ADD-QIA: An Adaptive Data Deduplication Framework Based on Quantum Immune Algorithm | 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 ADD-QIA: An Adaptive Data Deduplication Framework Based on Quantum Immune Algorithm Sashi Tarun, Shivani Goel, Vijaya Chandra Jadala This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7829113/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 Cloud computing has become the backbone of modern data management, yet the exponential growth of unstructured data from IoT devices, virtual machines, and enterprise systems has created excessive redundancy. Conventional deduplication techniques, such as fixed-size and content-defined chunking, either miss shifted duplicates or impose heavy computational overhead, limiting their scalability. Bio-inspired approaches (GA, PSO, IA) introduce adaptability but suffer from slow convergence and suboptimal trade-offs between deduplication ratio, execution time, and memory usage. To address these gaps, this paper proposes ADD-QIA, an Adaptive Data Deduplication framework based on a Quantum Immune Algorithm. By combining quantum-inspired probabilistic encoding with immune clonal selection, ADD-QIA dynamically adjusts chunking strategies to workload variations. Extensive evaluation on VM snapshots, enterprise backups, and cloud traces demonstrates that ADD-QIA achieves a deduplication ratio of 5.3:1, reduces execution time by 20%, and lowers memory usage by 15%, while sustaining throughput above 300 MB/s. The strength and scalability of the method is statistically validated. These findings make ADD-QIA a viable and scalable model of removing redundancy in the cloud. Adaptive Deduplication Quantum Immune Algorithm Cloud Storage Redundancy Elimi- nation Bio-inspired Quantum Computing Metaheuristics Cloud Effciency 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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