Citation :

Mirza Equtidar Husain, Imran Hussain, Safdar Tanweer, Ihtiram Raza Khan, "DRAPE: A Performance-Based Database Model for Cloud Environment," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 6, pp. 309-327, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I6P122

Abstract

Database performance is one of the most crucial topics in the IT industry. To address the performance-related issues with the Database, this research introduces DRAPE, a novel performance-based database model for the Azure cloud environment. The proposed model suggests key performance metrics configuration at the time of the database provisioning, such as resource sharing, artificial intelligence integration with DBaaS, elastic pool, and deployment model selection, database configuration like MAXDOP and QOF, along with cost optimization techniques resulting in enhancement of the database performance. Experimental results indicate a 50% reduction in query execution time, a 60% improvement in CPU compile time, an average increase of 23% in resource utilization, and an estimated cost benefit of 72%. The DRAPE is a multilevel performance evaluation system spanning database configuration, along with demonstration of the best execution plan for a query, cost savings, resource utilization in Elastic Pool, and calculation of the number of execution plans generated based on tables participating in a query using permutation theory. In contrast to existing research that mainly focuses on isolated performance key metrics, such as storage or computation. This research presents a novel paradigm that consists of a complete set of database optimization strategies with a cost-efficient design. This research is worthwhile in academia as well as in industry, demonstrating measurable performance gains across OLTP and OLAP environments. The database administrators, researchers, educators, data scientists, data analysts, and database developers stand to benefit significantly from its insights and practical applications.

Keywords

Cloud Computing, Cost Optimization, DBaaS, Database Performance, Elastic Pool, MAXDOP.

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