Evaluating the Performance of NoSQL Databases for Big Data in Cloud Computing Environments
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This study aims to evaluate the performance of NoSQL databases in distributed cloud computing environments, addressing the lack of comprehensive benchmarking in this domain. Specifically, it investigates MongoDB and Riak KV, two widely used NoSQL systems, across diverse cloud platforms, including Google Cloud, DigitalOcean, and OpenStack. Using the Yahoo Cloud Serving Benchmark, we designed and implemented a benchmarking model to measure key performance indicators, including latency, throughput, and scalability, under varying workloads and data sizes. The analysis revealed that MongoDB integrated with Google Cloud consistently outperformed other configurations, demonstrating superior throughput and lower latency in read and write operations. In contrast, Riak Key Value generally exhibited higher latency, especially in scan-intensive workloads. To support practical decision-making, a decision tree model was developed based on empirical findings to guide optimal selection of cloud computing platforms and databases. The proposed benchmarking framework is modular and extensible, allowing adaptation to other NoSQL technologies, cloud providers, and performance metrics. This research presents a novel, systematic methodology for evaluating NoSQL database performance in cloud environments, providing actionable insights for selecting high-performing, scalable solutions in big data applications. This modular design enables the addition of more database technologies, deployment options, and performance standards in the future, thereby supporting broader research and real-world applications in distributed systems and cloud computing.
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