How to Cite:
Angelina Jonah, "Unified Observability and Automation Framework for Autonomous Infrastructure Management" International Journal of Humanities Science Innovations and Management Studies, Vol. 1, No. 1, pp. 8-13, 2026.

Abstract:
The rapid evolution of cloud-native architectures, distributed computing, container orchestration, and hybrid multi-cloud deployments has significantly increased the complexity of modern IT infrastructure management. Traditional infrastructure monitoring solutions are often limited to isolated observability functions and reactive incident management, resulting in prolonged downtime, operational inefficiencies, and increased maintenance costs. This research proposes a Unified Observability and Automation Framework (UOAF) for Autonomous Infrastructure Management that integrates comprehensive observability, artificial intelligence for IT operations (AIOps), predictive analytics, knowledge-driven decision support, and automated remediation into a unified platform. The proposed framework combines telemetry collection, distributed tracing, metrics analysis, log intelligence, anomaly detection, root-cause analysis, and policy-based automation to enable autonomous infrastructure operations. Machine learning algorithms continuously learn from operational data, allowing predictive failure detection and intelligent resource optimization. The framework further integrates Infrastructure-as-Code (IaC), event-driven orchestration, and closed-loop automation for self-healing capabilities. Comparative analysis demonstrates that the proposed framework significantly reduces incident response time, improves infrastructure availability, enhances resource utilization, and lowers operational overhead compared with conventional monitoring systems. The research contributes a scalable reference architecture suitable for enterprise cloud environments, hybrid infrastructures, edge computing platforms, and large-scale digital transformation initiatives.

Keywords: Autonomous Infrastructure Management, Unified Observability, AIOps, Cloud Computing, Automation Framework, Predictive Analytics, Infrastructure Monitoring, Self-Healing Systems.

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