Autonomous Aiops Framework For Predictive Incident Management In Large-Scale Enterprise Systems

Abstract

Enterprise systems, such as cloud or distributed systems, generate immense amounts of log, metric, trace, and event data that are not easily handled by traditional incident management processes. When such issues are not resolved within their service-level targets (SLAs/SLOs), they lead to Service Interruptions, Alert Fatigue, delays, and impacts on operational efficiency and business continuity. In this paper, we describe this Autonomous AIOps Framework for Predictive Incident Management, which brings together the four pillars of observability, machine learning, root cause analysis, and self-healing within a single operational intelligence platform. The framework can be continually streamlined to be more efficient at collecting diverse operational data, detecting anomalies, predicting potential degradation targets, and identifying probable root causes of the anomalies, as investigated in a dependency-aware analysis. To improve the accuracy of their forecasts and predictions of incidents, they employ a hybrid predictive engine, which fuses the results from a combination of statistical, machine learning, and deep learning to deliver more reliable predictions of incidents, and an autonomous decision layer that enables the raising of intelligent remediation actions, without the need for human contact. The Recommended Framework would aim to enhance stability, reliability, and availability of services when using the Operational/tailored approaches, whilst reducing the Mean Time to Detect (MTTD), Mean Time to Resolve (MTTR), and operational overhead. The entire system is evolving toward a closed-loop, self-adaptive system that will enable proactive, resilient IT operations across a scalable enterprise IT infrastructure, supporting next-generation autonomous digital operations.