AI Driven Root Cause Analytics for Predictive Monitoring in Microservice Systems

Abstract

Microservice architecture has become a fundamental design paradigm in modern cloud-native applications because of its scalability, flexibility, and modularity. However, the distributed and dynamic nature of microservice systems introduces significant operational challenges, including service failures, dependency issues, latency bottlenecks, cascading errors, and infrastructure instability. Traditional monitoring techniques based on static rules and threshold alerts are often insufficient for detecting complex anomalies and identifying root causes in real time. Artificial Intelligence (AI) has emerged as an effective solution for intelligent observability and predictive monitoring in distributed systems. This research focuses on AI-driven root cause analytics for predictive monitoring in microservice environments. The study explores the integration of machine learning, deep learning, anomaly detection, distributed tracing, and graph-based analytics to predict failures and automatically identify the underlying causes of performance degradation. The proposed framework collects telemetry data from logs, metrics, traces, and events generated across microservices and applies AI algorithms to detect abnormal patterns and forecast incidents before they impact users. The research highlights the advantages of predictive analytics in reducing downtime, improving service reliability, accelerating incident response, and enabling self-healing capabilities. The findings demonstrate that AI-driven monitoring systems significantly outperform traditional monitoring methods in terms of accuracy, adaptability, scalability, and operational efficiency within cloud-native environments