Scalable Cloud Architectures Empowered by Machine Learning Intelligence

Abstract

Scalability and efficiency are critical factors in modern cloud architectures to meet the demands
of dynamic workloads and user requirements. This paper explores the integration of machine
learning intelligence to empower scalable cloud architectures, enabling automatic resource
provisioning, workload optimization, and adaptive infrastructure management. Through a
comprehensive review of existing literature and case studies, we elucidate the role of machine
learning algorithms in enhancing the scalability, performance, and reliability of cloud
infrastructures. The proposed approach aims to address the challenges of resource allocation, load
balancing, and system optimization in large-scale cloud environments, paving the way for nextgeneration cloud platforms capable of adapting to changing demands and maximizing resource
utilization.