A Review of Machine Learning Approaches for Real-Time Alerting in Service Level Objective Monitoring

Site reliability engineers manage increasingly complex systems and integrations, often under formal service level agreements (SLAs). A key challenge is balancing alerting strategies to detect service level objective (SLO) breaches-ensuring timely incident response while avoiding overly aggressive rules that lead to alert fatigue. Artificial Intelligence for IT Operations (AIOps) is a research domain focused on enhancing the maintainability and availability of IT systems using machine learning. In this study, we conducted a literature review to define the problem, assess state-of-the-art research in AIOps, and explore advancements in time-critical time series analysis. This research outlines key requirements for machine learning-based SLO alerting, identifying real-time failure detection and failure prediction as primary approaches. The length of the observation window, forecast horizon, and training/inference times are crucial parameters for selecting and optimizing solutions. We highlight machine learning techniques that align with these requirements and address known AIOps challenges. Our findings contribute to a better understanding of the problem space and potential solutions, helping researchers and industry practitioners drive innovation in machine learning-based reliability engineering.