Autonomous Cloud Optimization Leveraging AI-Augmented Decision Frameworks

Abstract

As the cloud environment continues to grow increasingly complex, more dynamic workloads cannot be addressed with the old models of automation, including static provisioning and threshold-driven automation. In this paper, the authors are going to explore the opportunities that AI-enhanced decision systems provide to optimize cloud resources on their own. It does not put AI at the top of the decision-making infrastructure loop but at the bottom. The data ingestion pipelines, inference services, and feedback loops are some of the key architectural elements that have been described in the paper, and when combined as a team, they allow adaptive resource allocation. Special attention is paid to the governance and reliability aspects, including explainability of the decisions, auditability, and implementing safety constraints, and the autonomous systems in question adhere to the enterprise policy. The paper is devoted to the role of AI as a means to redesign cloud infrastructure and make it more responsive by means of automation. But it may equally be proactive and self-reinforcing, and improve the effectiveness and flexibility of cloud resource management.