Optimizing Network Performance: Distributed Load Balancing VNF Scaling in SDN-Based Cloud Infrastructures

Optimizing Network Performance: Distributed Load Balancing VNF Scaling in SDN-Based Cloud Infrastructures | 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 Optimizing Network Performance: Distributed Load Balancing VNF Scaling in SDN-Based Cloud Infrastructures Zhang Jianping, Li Wei, Priya Sharma, Ahmed Hassan, Maria Gonzalez, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7310630/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 rapid growth of cloud computing and Software-Defined Networking (SDN) has necessitated efficient resource management to handle dynamic traffic demands in large- scale datacenters. This paper proposes a distributed framework for joint load balancing and Virtual Network Function (VNF) scaling to mitigate overload and underload condi- tions in SDN-based cloud environments. By leveraging the Alternating Direction Method of Multipliers (ADMM) and heuristic optimization techniques, our approach minimizes deployment and forwarding costs while ensuring efficient resource utilization. We formu- late the problem using Mixed Integer Linear Programming (MILP) and relax it into linear programming (LP) models to reduce computational complexity. Performance evaluations demonstrate that our method achieves faster convergence and lower resource overhead compared to centralized approaches, validated through simulations on k-fat-tree topolo- gies. Our findings highlight the potential of distributed optimization for scalable and robust network management in modern datacenters. Theoretical Computer Science Computer Architecture and Engineering Software-Defined Networking Virtual Network Functions Load Balancing Distributed Optimization ADMM Cloud Computing Resource Allocation 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-7310630","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496654913,"identity":"1551ec28-0337-4d0f-b4de-c8ad775889ad","order_by":0,"name":"Zhang 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