AI-BASED DISTRIBUTED SYSTEMS OBSERVATION SYSTEM: REAL-TIME MONITORING AND INTELLIGENT ANOMALY DETECTION FOR CLOUD INFRASTRUCTURE

Abstract

Distributed systems have become the foundation of modern computing infrastructure, powering cloud services, microservices architectures, and large-scale applications serving billions of users worldwide. However, the complexity, scale, and dynamic nature of these systems present significant challenges for effective monitoring, fault detection, and performance optimization. This research presents an AI-based distributed systems observation system that employs machine learning algorithms, anomaly detection techniques, and predictive analytics to provide comprehensive real-time monitoring and intelligent diagnostics. The system integrates data from multiple sources including application logs, system metrics, network traffic, and trace data to build holistic understanding of distributed system behavior. Through implementation of unsupervised learning for anomaly detection, deep learning for pattern recognition, and causal analysis for root cause identification, the system achieved 93.8% accuracy in detecting system anomalies, 89.4% accuracy in predicting performance degradation before user impact, and reduced mean time to resolution by 67% compared to traditional monitoring approaches. Validation across three production distributed systems processing over 50 million transactions daily demonstrated the system's effectiveness in identifying issues ranging from resource exhaustion and network partitions to configuration errors and cascading failures. This research contributes practical methodologies for applying AI technologies to distributed systems management and demonstrates significant improvements in system reliability, operational efficiency, and user experience.