Predictive Capacity Planning and Cost Optimization for Polyglot Cloud Databases Using Machine Learning

Abstract

Polyglot persistence has redefined the data management environment and now organizations can mix various database paradigms under the same cloud ecosystems. Nevertheless, this architectural flexibility brings along new operational issues- especially during capacity planning, cost optimization and predictability of performance. The given paper suggests a machine-learning-infused model of predictive resource distribution and real-time cost optimization in polyglot cloud database settings. Basing on the works related to cloud economics, NoSQL management, ML-based tuning, serverless computing, and hybrid data lakes, this study combines the time-series forecasting, cost-aware optimization, and performance modeling into a single system. Simulation experiments on heterogeneous workloads show that the accuracy of resource utilization, cost variability, and system performance are greatly improved over heuristic-based scaling, which has been used traditionally. The findings indicate that an AI-based system of dealing with polyglot clouds data bases in the contemporary distributed setting is feasible.