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Intelligent Cloud Infrastructure Management Using AI-Driven Observability and Event-Driven Architectures: A Unified Framework Leveraging DynamoDB, SQS, EKS, Redis, and Multi-Region Deployment Strategies

Author(s) Naresh Reddy Telukutla
Country United States
Abstract The rapid migration to cloud-native architectures has introduced unprecedented levels of complexity in infrastructure management. Traditional monitoring and reactive management tools are inadequate for the dynamic, ephemeral, and distributed nature of modern microservices and serverless ecosystems. This paper proposes a unified framework for intelligent cloud infrastructure management that synergistically combines AI-driven observability with event-driven architectures (EDA). The framework leverages a robust technology stack, including Amazon DynamoDB for stateful, low-latency metadata management, Amazon Simple Queue Service (SQS) for resilient asynchronous communication, Amazon Elastic Kubernetes Service (EKS) for container orchestration, and Redis for ultra-fast caching and real-time state propagation. Furthermore, it incorporates multi-region deployment strategies to ensure global resilience, low latency, and high availability. The proposed architecture moves beyond simple threshold-based alerting by employing machine learning models for anomaly detection, root cause analysis, and predictive auto-scaling, all orchestrated through a central event bus. This paper details the system architecture, core components, and implementation strategies, demonstrating through quantitative analysis how this unified approach reduces mean time to detection (MTTD) and mean time to resolution (MTTR) while optimizing resource utilization and operational costs.
Keywords AI-Driven Observability, Event-Driven Architecture, Cloud Infrastructure Management, DynamoDB, Amazon SQS, Amazon EKS, Redis, Multi-Region Deployment, Predictive Auto-scaling, Root Cause Analysis.
Field Engineering
Published In Volume 7, Issue 1, January 2026
Published On 2026-01-03
DOI https://doi.org/10.70528/IJLRP.v7.i1.2143
Short DOI https://doi.org/hbzxjt
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