Authors: Ojasvi Pandey
Abstract: The escalating complexity of multi-cloud and hybrid enterprise environments has rendered traditional, reactive infrastructure management obsolete. This review article investigates the transformation of cloud governance through the integration of predictive analytics and machine learning (ML) algorithms. We evaluate how supervised learning for workload forecasting, unsupervised learning for anomaly detection, and reinforcement learning for autonomous scaling address the competing priorities of cost, performance, and availability. The study details a theoretical framework for the cloud resource management lifecycle and proposes an AI-driven architecture that utilizes real-time telemetry data to execute self-healing remediations. Furthermore, we address critical technical constraints, including data veracity, model drift, and the computational overhead of ML engines. By exploring future trajectories such as green computing optimization and quantum-accelerated resource allocation, this article provides a strategic roadmap for organizations aiming to achieve total cloud autonomy. Ultimately, we demonstrate that predictive optimization is the essential mechanism for transforming cloud infrastructure into a proactive, self-adjusting asset that delivers maximum business value with minimal operational expenditure.
DOI: http://doi.org/10.5281/zenodo.18231353
