Performance Tuning Cloud-Hosted Databases: Resource Allocation & Query Optimization
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I4P116Keywords:
Cloud Databases, Performance Tuning, Resource Allocation, Query Optimization, Database Elasticity, Cost Efficiency, Cloud Computing, Machine LearningAbstract
What used to be a relatively simple task of data management has been substantially complicated by the rapid widespread transition to cloud-hosted database technologies such as Microsoft Azure SQL, Google Cloud Spanner, and other managed relational platforms, where the major selling points are scalability, availability, and flexibility. Nevertheless, this development has resulted in recurring performance issues that are inherently due to inefficient resource allocation and poorly optimized queries. Such inefficient practices can cause latency to be increased, throughput to be unpredictable, and inflow of money for operational purposes to be much higher than before, these are the problems cloud environment elasticity users fail to solve. In response to these issues, this work develops a hierarchical improvement framework that combines performance tuning with dynamic resource allocation and AI-guided query optimization. Our compound method uses the proposed model to leverage predictive tools for on-demand distribution of operands and memory together with machine learning to continuously refine each execution plan, user behavior, or workload pattern. Through a series of tests on various cloud platforms, we convincingly demonstrate performance enhancements such as throughput increase by 40%, query latency reduction by 35%, and operational costs decrease by 25%, which are not achievable to the same extent by static tuning methods only. Along with tangible performance enhancements, the paper advocates that in the present-day database world, continuous monitoring, and automation are indispensable gears of the machinery. Cloud database administrators can implement this proposition that delivers them a guide, stepping aside from the empiric way, rather on a data-driven way, which unites performance with cost efficiency and leads to the intelligent tuning.
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