Selecting Cloud Storage Models Based on Empirical Performance Evidence

Public cloud platforms provide multiple storage abstractions, including object storage, block storage, and managed file systems, each designed to balance scalability, durability, and operational flexibility. Despite their widespread adoption, selecting an appropriate storage model for data-intensive workloads remains a non-trivial architectural decision due to differences in latency, throughput, and I/O behavior. This paper presents a systematic empirical evaluation of these storage abstractions under controlled experimental conditions to provide evidence-based guidance for workload-driven storage selection. We benchmark sequential, random, and mixed I/O workloads, measuring sustained throughput, input/output operations per second (IOPS), latency characteristics, and performance stability. The results reveal distinct performance profiles across storage models. Block storage consistently delivers high IOPS and low latency, making it suitable for transactional and latency-sensitive workloads. Object storage demonstrates strong performance for large sequential transfers and bulk data movement. Managed file storage provides shared access semantics with moderate performance trade-offs. Rather than advocating a universally superior storage solution, the findings emphasize aligning storage selection with dominant workload characteristics. By quantifying observable performance differences across abstractions, this study provides practical architectural insights for analytics systems, distributed applications, and cloud-native data platforms.