Autonomous DevOps and AIOps Frameworks for Intelligent Cloud Monitoring Incident Response and Cost Optimization

Abstract

Autonomous DevOps and AIOps frameworks are emerging as important approaches for managing the increasing complexity, scale, and dynamism of contemporary cloud environments. Traditional cloud monitoring and incident-response practices often depend on manually configured alerts, fragmented observability tools, and human intervention, resulting in delayed detection, prolonged service disruptions, and inefficient resource utilization. Autonomous DevOps integrates continuous integration, continuous delivery, infrastructure automation, observability, and policy-driven remediation, while AIOps applies artificial intelligence and machine learning to operational data to identify anomalies, correlate events, predict incidents, and support automated decision-making. This paper examines an integrated framework for intelligent cloud monitoring, automated incident response, and cost optimization. The proposed research methodology combines literature analysis, framework design, simulated cloud workloads, operational metrics, and comparative evaluation. The framework incorporates telemetry collection, anomaly detection, event correlation, root-cause analysis, automated remediation, resource-rightsizing, and cost-aware orchestration within a continuous feedback loop. Evaluation focuses on incident detection accuracy, mean time to detection, mean time to recovery, false-positive rates, resource efficiency, and cloud expenditure. The study argues that combining Autonomous DevOps with AIOps can provide a systematic foundation for self-monitoring, self-healing, and cost-aware cloud operations while maintaining human oversight for high-risk decisions.