Authors: Sana Rahman
Abstract: Cloud infrastructure optimization has become a critical area of research and development as organizations increasingly rely on cloud computing for scalable, flexible, and cost-effective IT services. Efficient utilization of cloud resources is essential to reduce operational costs, improve performance, and ensure high availability of services. This study reviews various cloud infrastructure optimization techniques, including resource allocation, load balancing, auto-scaling, virtualization, and energy-efficient computing strategies. It also examines the role of artificial intelligence and machine learning in enhancing optimization through predictive analytics and intelligent decision-making. The paper highlights how cloud providers manage computing, storage, and network resources to achieve optimal performance under dynamic workloads. Furthermore, it discusses key challenges such as resource wastage, latency, workload unpredictability, and security constraints. Emerging trends such as serverless computing, edge-cloud integration, and AI-driven cloud management are also explored. The findings emphasize that effective optimization techniques are essential for improving efficiency, scalability, and sustainability in modern cloud infrastructures.
DOI: http://doi.org/
