AI-Driven Infrastructure Scaling for Cost Optimization in Cloud Environments: A Systematic Review

Abstract

This article comprehensively analyzes AI-driven infrastructure scaling for cost optimization in cloud environments. We examine how machine learning algorithms can dynamically adjust cloud resources based on historical patterns and real-time workload demands, addressing the persistent challenge of balancing performance requirements with cost efficiency. The article analyzes various scaling mechanisms, including historical pattern analysis, real-time monitoring systems, and decision-making algorithms for resource adjustment, alongside predictive analytics approaches for workload forecasting. Through multiple case studies across diverse industry sectors, the article identifies best practices, implementation challenges, and integration considerations for organizations adopting these technologies. The article also explores emerging directions, including serverless architecture integration, multi-cloud optimization strategies, and edge computing applications. The article's findings indicate that AI-driven infrastructure scaling represents a significant advancement in cloud resource management, enabling organizations to optimize their cloud expenditure while maintaining application reliability and performance.