From Logs to Intelligence: Engineering Self-Healing Cloud Ecosystems with AI-Powered Observability

Abstract

The recent and incessant fast-paced evolution of cloud-native computing has altered the current state of computing and has quickly made it a highly-distributed, dynamic and complex ecosystem. Although this change has resulted in the advent of a new level of scalability, and flexibility it has not come without its fair share of gravitas on the operational issues which have been largely on the monitoring, diagnosing and reliability of the system. The ancient ways of keeping track of fixed boundaries and the human operator cannot respond to the size and indistinctness of the new cloud world. In its turn, observability has become a paradigm of paramount importance since it allows learning more about a system behavior by analyzing logs, metrics, and traces. This review has discussed the shift towards traditional observability to AI-driven observability and how machine learning can help to turn raw telemetry data into actionable intelligence. The paper has demonstrated how the anomaly detection, log analysis, or distributed tracing and predictive modeling have been implemented into the development of self-healing ecosystems in the cloud through the synthesis of literature available. These systems are capable of automatically detecting abnormalities and diagnosing root causes and undertaking remediation measures at the least human intervention. The conceptual framework, Intelligent Observability-to-Healing (IOH) model, that connects the telemetry visibility, contextual intelligence, decision confidence, execution automation, and adaptive learning to a governance boundary has also been mentioned in the review. Prior experimental studies also supported the view that the incorporation of AI into observability pipelines can lead to a much higher accuracy of detecting anomalies, a decrease in the time spent on incident responses, and the ability to proactively manage the system. Nonetheless, the paper also determined some important challenges such as the heterogeneity of the data, the interpretability of the models, the limitation of scalability, alert fatigue, and the confidence in autonomous decision-making. These issues demonstrate the necessity of more powerful, interpretable and policy conscious AI systems. Altogether, the current review is a contribution to the increasing body of knowledge as it offers a well-organized and humanistic view of the possibility of intelligent observability as a way of facilitating resilient, adaptive, and self-healing cloud infrastructures.