A Predictive Approach to Cloud Storage Cost Optimization

Cloud storage has become an essential component of modern data management, but increasing storage costs present a significant challenge for organizations. Conventional tier-based storage systems necessitate manual distribution, resulting in potential inefficiencies and increased expenses. This study presents a forecasting model for intelligent data tiering, utilizing machine learning to automate storage selections based on access frequency. Utilizing historical usage patterns, the model automatically categorizes data into three storage tiers: hot, warm, or cold, thereby balancing cost-effectiveness and data retrieval speed. The suggested framework incorporates predictive analysis to decrease operational costs and enhance the use of cloud resources. The experimental data show that the model is highly effective in predicting data access patterns, resulting in significant financial savings when compared to traditional storage management methods. Performance evaluations show that predictive tiering reduces latency and improves scalability. This research offers a practical, data-driven strategy for cloud service companies and businesses looking to improve their storage infrastructure. Organizations can achieve a sustainable balance between cost savings and ensuring efficient long-term data management practices.