Measuring the Impact of Cloud Database Modernization on End-to-End Performance in Consumer-Scale Digital Applications
DOI:
https://doi.org/10.15662/IJARCST.2023.0605008Keywords:
Cloud Database Modernization, Distributed Databases, End-to-End Performance, Microservices Architecture, Tail Latency, Polyglot Persistence, Elastic ScalabilityAbstract
The rapid global expansion of consumer-scale digital applications has intensified the demand for scalable, low-latency data management architectures. Cloud database modernization—defined as the systematic transition from legacy monolithic databases to distributed, cloud-native, dynamically scalable systems—has emerged as a critical enabler of high-performance applications. Yet, despite the strategic prominence of modernization initiatives, scholarly understanding of how such transformations concretely influence end-to-end application performance remains fragmented. This research synthesizes theoretical models of distributed data storage, empirical performance studies, and architectural analyses to construct an integrated framework for measuring the performance impact of cloud database modernization. Building on research in distributed systems, cloud elasticity, NoSQL and NewSQL architectures, microservices, and cloud performance modeling, the study develops a rigorous methodology emphasizing controlled experimentation, multilevel observability, and workload-specific performance evaluation. The analysis reveals that modernization enhances throughput, elasticity, and tail-latency predictability under most workload conditions, but can also introduce new bottlenecks related to inter-service communication, consistency semantics, and resource contention. The paper concludes by outlining implications for researchers and practitioners and providing directions for future empirical validation.
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