Efficient Storage Solutions for Big Data in Cloud Environments: A Comparative Study of Scalability, Cost, and Performance

Abstract

Cloud computing infrastructures have become the de facto standard for hosting massive data repositories, driven by the exponential expansion in data generation across diverse domains. The pressing need to handle this influx of data has motivated new strategies for storage systems that must remain operationally feasible, scalable, and cost-effective. Designing storage mechanisms for big data in cloud environments requires sophisticated techniques to maintain performance guarantees, allow elastic resource allocation, and ensure minimal latency under shifting workloads. Moreover, providers face challenges associated with ensuring high availability, load balancing, fault tolerance, and consistent data integrity. A key factor in formulating these architectures involves reconciling theoretical models with implementation realities, such that overheads remain within acceptable bounds for both batch workloads and interactive, low-latency queries. In this paper, a thorough comparative investigation is presented, focusing on the interplay among scalability, cost efficiency, and performance optimization in modern storage systems. By exploring cutting-edge theoretical frameworks and examining the interplay between mathematical abstractions and hardware-level constraints, this work aims to shed light on the design choices that best match different application demands. Through a careful synthesis of model formulations, computational analysis, and large-scale practical considerations, this study highlights fundamental performance trade-offs while paving the way for robust, future-proof storage solutions.