The Adaptive Intelligence in Cloud Systems: A Unified Architecture for AI Enhanced Observability and Automated Root Cause Analysis
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I1P115Keywords:
Adaptive Intelligence, Aiops, Observability, Distributed Tracing Log Analytics, Automated Root Cause Analysis, Predictive Resilience, Kubernetes, Cloud OperationsAbstract
Modern cloud systems generate high volume telemetry across logs traces metrics events and configuration state. Observability platforms can collect and visualize these signals yet interpretation triage and remediation still depend heavily on human expertise. Manual root cause analysis static dashboards and rule based alerts struggle with the complexity of microservices event driven pipelines and distributed runtime variability. This paper proposes a unified architecture for adaptive intelligence in cloud systems that integrates an observability pipeline lightweight learning models and autonomous decision policies to achieve continuous self analysis fault localization and performance optimization. The architecture enables applications to learn behavioral baselines detect degradations early identify root causes through dependency aware reasoning and apply corrective actions such as dynamic throttling configuration tuning resource scaling and workflow rerouting. We provide a concrete implementation blueprint including data normalization feature engineering diagnosis scoring and policy guardrails. We also describe an evaluation plan that targets improved mean time to detect and mean time to recover while reducing alert noise and controlling automation risk. The results argue that embedding adaptive intelligence directly into the operational stack is becoming essential for predictable resilience in large scale cloud services
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