Feature Selection Methods for Intelligent Software Performance Monitoring Based on Machine Learning

This paper presents a set of feature selection methods for intelligent software performance monitoring based on machine learning models, with a focus on improving interpretability, scalability, and adaptability in high-dimensional telemetry analysis. The research addresses limitations of traditional statistical and rule-based approaches, which are often unable to capture nonlinear dependencies and dynamic interactions in modern distributed architectures. A unified methodology is proposed that integrates several complementary techniques for adaptive feature selection in intelligent monitoring systems. These include a topology-aware method based on graph neural networks for modeling structural dependencies in microservice architectures, a correlation-driven approach for reducing feature redundancy, a multifactor fusion method combining statistical significance, temporal stability, and predictive contribution, a cost-efficient strategy for serverless environments, and a context-aware reinforcement learning approach for dynamic feature adaptation. The proposed methods are evaluated on a large-scale dataset exceeding 3.5 TB, collected from 42 real-world applications representing monolithic, microservice, cloud-native, and serverless architectures. The results show an average reduction in feature dimensionality of 37%, while maintaining over 95% predictive accuracy across multiple models. Additional improvements include, on average, a 21% increase in dependency modeling accuracy, an 18% gain in feature relevance estimation, a 26% reduction in feature instability under dynamic workloads, and up to 42% cost reduction in serverless environments, as observed across repeated experiments under controlled workload variability and consistent evaluation settings. While the results demonstrate the effectiveness of adaptive feature selection, further validation in diverse real-world conditions is required to confirm the generalizability of the proposed framework.