Software Performance Monitoring Using Machine Learning Algorithms

Software performance monitoring is crucial for maintaining system efficiency and reliability. Traditional methods rely on static rule-based approaches, which may not effectively predict performance degradation. This study proposes a machine learning (ML)-based approach using Random Forest, Support Vector Machine (SVM), and Neural Networks to detect and predict software performance anomalies. The proposed system analyzes real-time metrics like CPU usage, memory consumption, request latency, and disk I/O, improving fault detection and system stability. Experimental results demonstrate that ML algorithms can significantly enhance performance monitoring, with Random Forest achieving the highest accuracy. This paper also discusses the challenges of real-time performance monitoring and provides a comparative analysis of different ML models.