Log2Learn: Intelligent Log Analysis for Real-Time Network Optimization

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.

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Published
2025-04-25
DOI
https://doi.org/10.1109/icaid65275.2025.11034497
Primary Topic
Software System Performance and Reliability
Type
article
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article

Log2Learn: Intelligent Log Analysis for Real-Time Network Optimization

Tongwei Tu
Software System Performance and Reliability
article

Log2Learn: Intelligent Log Analysis for Real-Time Network Optimization

Tongwei Tu
article en

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.

Embry–Riddle Aeronautical University (US)
Openalex Percentile: Top 85%
Software System Performance and Reliability
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