"Energy-Efficient Query Processing Strategies for Distributed Big Data Systems"

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Tauqeer Akhtar


Energy consumption associated with big data processing has become a critical issue as the scale and complexity of distributed big data systems increase. A number of strategies are explored in this paper to enhance energy efficiency in query processing in such systems. By minimizing data movement, implementing cost-based and adaptive query execution plans, and optimizing resource utilization through dynamic scaling and scheduling, some key strategies can be implemented. Caching and materialized views also contribute to energy savings by using energy-conscious hardware, utilizing algorithmic improvements like approximate and batch processing, and leveraging energy-aware hardware. The presentation also examines energy-aware query scheduling, renewable energy-powered data centers, and advanced cooling technologies. Enhancements in efficiency are achieved through software optimizations, continuous monitoring, and feedback loops. Several examples illustrate the practical applications of these strategies, including Apache Hadoop, Apache Spark, Greenplum Database, and Microsoft SQL Server. A holistic approach is the key to reducing an organization's energy footprint, reducing operational costs, promoting environmental sustainability, while maintaining high performance.

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