Log2Learn: Intelligent Log Analysis for Real-Time Network Optimization

Log2Learn: Intelligent Log Analysis for Real-Time Network Optimization | 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Article Log2Learn: Intelligent Log Analysis for Real-Time Network Optimization Tongwei Tu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6565529/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 Large network infrastructures generate vast amounts of log data, presenting challenges in analysis and optimization. Traditional log management systems often struggle to keep pace with the rapid influx of information, leading to missed anomalies and performance issues. In this context, we introduce Log2Learn, an innovative framework that integrates advanced machine learning techniques for intelligent log analysis aimed at real-time network optimization. Log2Learn enhances predictive capabilities with a multi-layered approach, enabling the early detection of potential network failures and allowing administrators to implement proactive solutions. The continuous feedback loop mechanism in our framework updates analytical models with fresh data, adapting to changing network environments. Comprehensive evaluations across diverse network settings demonstrate that Log2Learn substantially diminishes downtime while boosting throughput. Computer Architecture and Engineering Log management systems Real-Time Optimization Feedback Machanism 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. 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