Predictive Database Scaling: AI Forecasting Models for Cloud Resource Optimization

Abstract

This article explores how predictive AI models are revolutionizing cloud database resource allocation by anticipating usage spikes before they occur. The article further analyzes various machine-learning techniques that identify temporal patterns in database workloads and automatically trigger scaling actions to maintain performance while minimizing costs. The research examines thorough data collection strategies, features engineering approaches, and model selection criteria for building powerful predictive scaling frameworks. Through examination of real-world implementations across e-commerce, financial services, and media streaming platforms, the article demonstrates how organizations have achieved substantial cost savings while eliminating performance degradation during peak usage periods. The article provides technical challenges and implementation best practices as practical guidance for database architects looking to implement AI-driven predictive scaling in their cloud environments.