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openalexComputers2026-07-24Cited by 0

Performance Evaluation of On-Premise SQL Server and Azure SQL Database Using a .NET 8 E-Commerce Application

Ahmed Jawad Kadhim, Tayseer S. Atia

Empirical comparisons between cloud-based and on-premise database deployments under realistic e-commerce workloads remain limited. This study presents a controlled experimental evaluation of Microsoft SQL Server 2022 (on-premise) versus Azure SQL Database, using an identical .NET 8 e-commerce application (ASP.NET Core Web API, Blazor WebAssembly) with the same architecture, schema, and dataset (5000 product records, 10,000 transaction records). Performance was evaluated for SELECT, INSERT, UPDATE, and DELETE operations under workloads of up to 50 concurrent users, with each operation repeated 30 times, measuring query response time, throughput, and CPU utilization. Statistical analysis used repeated-measures ANOVA with Greenhouse–Geisser correction, Bonferroni-adjusted post hoc comparisons, and independent-samples t-tests (p < 0.001), with effect sizes reported using Cohen’s d. Azure SQL Database consistently outperformed the on-premise deployment: average SELECT response time decreased by 55% (203.5 ms vs. 452.3 ms), and throughput increased by 101.6% (987.6 vs. 489.8 operations/s). Although Azure showed higher average CPU utilization (20.4% vs. 4.9%), this reflects its dynamic resource allocation rather than reduced efficiency. Stress testing with 100,000 product records, 500,000 transaction records, and up to 500 concurrent users confirmed Azure’s superior scalability, reducing peak latency from 4850.7 ms to 580.4 ms. These findings provide strong empirical evidence supporting cloud migration for high-transaction e-commerce applications requiring low latency and high throughput.

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crossrefComputers2025-11-23Cited by 1

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crossrefComputers2026-02-12

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crossrefComputers2026-02-02Cited by 4

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crossrefComputers2025-06-13Cited by 7

Intelligent Fault Detection and Self-Healing Mechanisms in Wireless Sensor Networks Using Machine Learning and Flying Fox Optimization

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WSNs play a critical role in many applications that require network reliability, such as environmental monitoring, healthcare, and industrial automation. Thus, fault detection and self-healing are two effective mechanisms for addressing the challenges of node failure, communicati…

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crossrefComputers2026-05-01Cited by 1

A Rigorous Comparative Study of Supervised Machine Learning Techniques for Network Anomaly Detection: Empirical Insights from the UNSW-NB15 Dataset

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The increasing complexity of modern network infrastructures has intensified the need for reliable and efficient intrusion detection systems. While advanced deep learning approaches have demonstrated strong performance, their high computational cost and limited interpretability re…

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crossrefComputers2025-09-16Cited by 16

Fake News Detection Using Machine Learning and Deep Learning Algorithms: A Comprehensive Review and Future Perspectives

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Currently, with significant developments in technology and social networks, people gain rapid access to news without focusing on its reliability. Consequently, the proportion of fake news has increased. Fake news is a significant problem that hinders societies today, as it negati…

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