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Analysis Study Research Paper | Computer Science & Engineering | United States of America | Volume 12 Issue 8, August 2023 | Rating: 5.1 / 10
Query Optimization for Big Data Workloads in Cloud-Enabled Distributed Databases
Chakradhar Bandla
Abstract: The exponential growth of big data has presented significant challenges in efficiently managing and processing workloads in cloud-enabled distributed databases. Query optimization is a critical aspect of ensuring high performance, reduced latency, and cost-effectiveness in such systems. This paper explores advanced techniques and strategies for optimizing queries tailored to the unique demands of big data workloads in distributed cloud environments. Key contributions include a comprehensive analysis of query optimization challenges in cloud-enabled distributed databases, the proposal of a novel cost-based optimization framework, and the integration of machine learning models to predict query execution plans dynamically. Experiments conducted on diverse big data benchmarks demonstrate significant improvements in query execution times and resource utilization compared to traditional optimization approaches. The findings highlight the potential of leveraging intelligent query optimization techniques to enhance the scalability and efficiency of distributed database systems in the cloud, addressing the growing demands of big data applications.
Keywords: Query Optimization, Big Data Workloads, Cloud Computing, Distributed Databases, Machine Learning, Performance Efficiency
Edition: Volume 12 Issue 8, August 2023,
Pages: 2576 - 2580