A Memory-Efficient Approach to Cost-Optimized Storage Tiering for Cloud Workloads

Abstract

As cloud computing continues to grow, managing and optimizing storage for cloud workloads becomes a crucial challenge. A significant concern in this domain is the cost associated with cloud storage, which can become overwhelming when dealing with vast amounts of data. Traditional storage management methods often fail to adequately address both performance and cost-efficiency, especially in dynamic and fluctuating workloads. This paper proposes a novel memory-efficient approach to cost-optimized storage tiering, designed specifically for cloud environments. The approach focuses on intelligently classifying data and dynamically migrating it across different storage tiers based on both usage patterns and cost considerations. By leveraging memory-efficient algorithms, this method minimizes overheads in managing large-scale datasets, ensuring optimal use of system resources. The solution utilizes machine learning techniques to predict access patterns and proactively allocate storage, balancing both cost and performance. Additionally, by optimizing storage tier assignments, the approach reduces unnecessary data movement, thus minimizing latency and energy consumption. The proposed model not only significantly reduces the overall cost of cloud storage but also maintains high levels of performance for diverse workloads. Experiments and simulations demonstrate that the memory-efficient approach outperforms traditional methods in terms of cost savings and resource utilization, making it an ideal solution for modern cloud environments dealing with increasingly complex and diverse workloads. This work offers an innovative strategy for cost-optimized cloud storage management while addressing scalability and memory efficiency concerns.