Abstract
Energy efficiency in analytical database systems is becoming increasingly important because of the rapid growth in energy consumed by data centers driven by the recent big data boom. Previous studies showed that processor speed scaling has the potential to improve energy efficiency of analytical queries. These results, however, were obtained from measurement of specific queries. The power–performance characteristics of processor speed scaling specific to analytical database systems still remains unexplored despite their importance in energy efficient analytical query processing. We tackle this problem by modeling the energy costs of analytical queries with processor speed scaling based on query processing throughput. Our experimental evaluation shows that our energy model can be fitted within an error of 1.65% and can be used to identify power–performance characteristics of analytical queries.
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Notes
- 1.
Although this assumption does not always hold on realistic environments, modeling power consumption as a time-varying function and considering variation of power consumption of other components are beyond the scope of this paper.
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Luo, B., Hayamizu, Y., Goda, K., Kitsuregawa, M. (2018). Modeling Query Energy Costs in Analytical Database Systems with Processor Speed Scaling. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2018. Lecture Notes in Computer Science(), vol 11030. Springer, Cham. https://doi.org/10.1007/978-3-319-98812-2_27
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DOI: https://doi.org/10.1007/978-3-319-98812-2_27
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