Intelligent event correlation and root cause analysis using ai in distributed it infrastructure

Abstract

Scalability and operational flexibility have been greatly enhanced by distributed systems and IT architectures, including cloud-native, microservices, hybrid cloud, and container-based approaches. These architectures, however, generate large volumes of events, alarms, logs, metrics, and traces of various types, making monitoring and root cause analysis (RCA) a time-consuming and difficult task to perform by hand. The management of such events through manual processes would typically rely on preconfigured rules and thresholds, resulting in ‘alert fatigue’, delayed incident response, and a lack of awareness of events. In this paper, an intelligent solution using Artificial Intelligence (AI) to correlate and analyze the Root Cause (RC) of various events in a distributed IT environment is introduced. The framework includes event normalization, machine learning models to implement correlation, graph modeling of dependencies, anomaly detection, and causal inference models that infer valid cause-and-effect relationships between infrastructure events and identify useful relationships among them. The proposed architecture utilizes data from various observability sources, minimizes noise, narrows down to high-priority incidents, and leverages this data to speed up incident response. Additionally, the framework includes steps to promote explainable AI, fostering trust and transparency in AI-driven decision-making. The research work showcases the possibilities of implementing intelligent event correlation to enable a deeper, more autonomous transition to AIOps-inspired IT operations, making IT operations faster, more efficient, and more future-proof.