Autonomous Cloud Operations through Predictive Observability and Machine Learning-Based Incident Intelligence
DOI:
https://doi.org/10.15662/IJARCST.2025.0806038Keywords:
Autonomous Cloud Operations, Predictive Observability, Machine Learning, Incident Intelligence, Cloud Computing, AIOps, Anomaly Detection, Root Cause Analysis, Automated Remediation, Cloud ResilienceAbstract
Cloud computing environments have become increasingly complex due to the widespread adoption of microservices, containers, serverless applications, distributed databases, hybrid infrastructures, and multi-cloud deployments. This complexity has created significant challenges for conventional IT operations, particularly in detecting failures, identifying their root causes, and responding to incidents before they affect business services. Predictive observability and machine learning-based incident intelligence provide an opportunity to transform cloud operations from reactive monitoring toward proactive and partially autonomous management. Predictive observability combines metrics, logs, traces, events, and contextual operational information to identify emerging anomalies and estimate potential failures before they become critical incidents. Machine learning can further support this process by identifying patterns in historical operational data, correlating apparently unrelated events, prioritizing incidents, and recommending appropriate remediation actions. This study examines a conceptual framework for autonomous cloud operations that integrates predictive observability, machine learning, automated incident intelligence, and controlled remediation. The proposed approach treats observability data as the foundation for continuously assessing cloud-system health and uses machine-learning models to transform operational telemetry into predictive insights. An incident-intelligence layer correlates anomalies, evaluates their potential impact, and supports automated or human-approved responses. The research adopts a qualitative conceptual methodology involving literature analysis, framework development, and scenario-based evaluation. Particular attention is given to prediction accuracy, root-cause analysis, automation reliability, explainability, false alerts, security, and human oversight. The study establishes a framework for improving cloud resilience while recognizing that autonomous operations require carefully defined control boundaries and continuous validation
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