Machine Learning-Driven Resource Orchestration for Fog and Edge Computing in SDN Environments

Machine Learning-Driven Resource Orchestration for Fog and Edge Computing in SDN Environments | 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 Machine Learning-Driven Resource Orchestration for Fog and Edge Computing in SDN Environments Emma Thompson, Rahul Patel, Lin Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7333528/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 Fog and edge computing, integrated with software-defined networking (SDN), provide a robust framework for supporting latency-sensitive applications in distributed environments. This paper proposes a machine learning-driven resource orchestration framework that optimizes task offloading and resource allocation across fog and edge nodes using a distributed optimization approach. By combining Lyapunov optimization with machine learning techniques, such as reinforcement learning and predictive modeling, and leveraging SDN’s centralized control, our method minimizes latency and energy consumption while ensuring quality of service (QoS). The framework employs a mixed integer non-linear programming (MINLP) model, enhanced with a heuristic-based relaxation for scalability, to manage resources dynamically in fog-enhanced edge networks. Simulations on fog-edge-cloud topologies demonstrate that our approach achieves lower latency and higher resource efficiency compared to centralized methods, validated through extensive performance evaluations, highlighting the role of machine learning in adapting to dynamic workloads and fog computing in extending computational capabilities closer to end-users. Theoretical Computer Science Computer Architecture and Engineering fog computing edge computing machine learning software-defined networking resource orchestration task offloading Lyapunov optimization distributed systems quality of service 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. 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