AI-driven Storage Optimization for Sustainable Cloud Data Centers: Reducing Energy Consumption through Predictive Analytics, Dynamic Storage Scaling, and Proactive Resource Allocation

Abstract

As the demand for cloud services and data storage grows exponentially, cloud data centers face immense pressure to improve efficiency in terms of energy consumption. This is a significant challenge, as traditional storage management methods often lead to suboptimal resource utilization and excessive energy waste. To address these inefficiencies, Artificial Intelligence (AI) and Machine Learning (ML) are emerging as powerful tools capable of transforming the way storage resources are managed. This paper provides a technical exploration of how AI and ML techniques can optimize data storage in cloud data centers, focusing on reducing energy consumption through three key approaches: predictive analytics, dynamic storage scaling, and proactive resource allocation. Predictive analytics, through advanced time-series models, can anticipate storage demand and optimize resource provisioning. Dynamic storage scaling uses reinforcement learning and adaptive algorithms to efficiently allocate storage resources in real-time based on fluctuating workloads. Proactive resource allocation, aided by AI models, coordinates storage with network and compute resources, ensuring that all aspects of the data center infrastructure operate in an energy-efficient manner. Although AI presents opportunities for substantial energy savings and improved storage performance, challenges persist in maintaining system reliability, managing data integrity, and balancing computational overhead. This paper details the mechanisms, algorithms, and architectural frameworks behind these AI-driven techniques, highlighting both their advantages and limitations.

AI-driven storage management, Cloud data centers, Dynamic storage scaling, Energy efficiency, Predictive analytics, Proactive resource allocation, Resource optimization