Paper Contents
Abstract
ABSTRACT AI-driven self-optimizing cloud systems enable autonomous monitoring, resource provisioning, and adaptive optimization beyond traditional approaches. Using machine learning, predictive analytics, and reinforcement learning, they support proactive workload balancing, cost-efficient scheduling, and fault tolerance. This review highlights key frameworks, challenges like scalability and security, and integration opportunities with edge computing and the Internet of Things. Advancing these systems requires multidisciplinary research to ensure efficiency, transparency, and trust.
Copyright
Copyright © 2025 Dr.D.Richard. This is an open access article distributed under the Creative Commons Attribution License.
Paper ID:
IJPREMS50900035810
ISSN:
2321-9653
Publisher:
ijprems