End-to-End Throughput Optimization of Secure Cloud Database Systems for High-Volume Application Traffic
DOI:
https://doi.org/10.15662/IJRAI.2020.0306003Keywords:
Secure Cloud Databases, Throughput Optimization, Transparent Data Encryption, Encrypted Query Processing, High-Volume Workloads, Distributed Replication, Multi-Layer Performance TuningAbstract
High-volume web and mobile applications depend increasingly on cloud-hosted database systems that must deliver high throughput while enforcing strong security controls such as encryption, fine-grained access control, and isolation in multi-tenant environments. While the literature on cloud databases, distributed storage, and database encryption has grown significantly over the last decade, there is still limited integrative guidance on how to optimize end-to-end throughput in secure cloud database deployments. This article synthesizes research up to 2020 on cloud database architectures, encrypted query processing, and encryption performance to propose a structured view of throughput bottlenecks and optimization strategies. We analyze how security mechanisms—transparent data encryption, client-side encryption, and encrypted query processing—affect CPU, I/O, and network behavior, and we discuss architectural patterns such as log-structured, network-aware storage (e.g., Aurora-style designs), replication strategies, and workload-aligned sharding that mitigate these costs. Building on this foundation, we outline a multi-layer optimization framework spanning application, middleware, database engine, and storage layers, emphasizing measurement-driven tuning and security–performance trade-offs. We conclude with open research questions around adaptive encryption, workload-aware security policies, and formal models that jointly capture security and throughput objectives.
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