AI-Driven AIOps for Proactive Incident Detection and Auto-Remediation in AWS Cloud Environments
DOI:
https://doi.org/10.63282/3050-9416.ICAIDSCT26-131Keywords:
AIOps, AWS Cloud, DevOps Automation, Cloud Operations Monitoring, Predictive Analytics, Anomaly detection, Auto remediation, Real-time Incident ResponseAbstract
Cloud-based enterprise systems generate vast volumes of operational data, including logs, metrics, and events, making manual monitoring and incident management increasingly challenging. This paper presents an AI-driven AIOps framework for proactive incident detection and automated remediation in AWS cloud environments, integrating machine learning and cloud-native services to enhance system reliability and operational efficiency. The framework leverages unsupervised and supervised learning techniques to detect anomalies in real-time across multiple services such as EC2, Lambda, CloudWatch, and Route 53. Detected anomalies trigger automated remediation workflows, reducing mean time to resolution (MTTR) and minimizing service downtime. The system also incorporates predictive analytics to forecast potential performance bottlenecks and resource constraints, enabling proactive capacity management and cost optimization. A prototype implementation demonstrates the effectiveness of the approach in a production-scale cloud environment, showing significant improvements in incident response time and overall system stability. By combining AI, DevOps practices, and AWS cloud infrastructure, this study provides a practical roadmap for intelligent operations (AIOps) in modern enterprise environments, highlighting the potential of AI to automate routine operational tasks while maintaining high reliability and efficiency. This work contributes to the fields of cloud operations, machine learning, and automated system management, offering insights for both academic research and real-world enterprise application.
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