Abstract
In the last few years, we have been seeing a significant increase in research about the energy efficiency of hardware and software components in both centralized and parallel platforms. In data centers, DBMSs are one of the major energy consumers, in which, a large amount of data is queried by complex queries running daily. Having green nodes is a pre-condition to design an energy-aware parallel database cluster. Generally, the most existing DBMSs focus on high-performance during query optimization phase, while usually ignoring the energy consumption of the queries. In this paper, we propose a methodology, supported by a tool called EnerQuery, that makes nodes of parallel database clusters saving energy when optimizing queries. To show its effectiveness, we implement our proposal on the top of PostgreSQL DBMS query optimizer. A mathematical cost model based on a machine learning technique is defined and used to estimate the energy consumption of SQL queries.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
e Sustainability Initiative, G., the Boston Consulting Group, I: Gesi smarter 2020: The role of ict in driving a sustainable future. Press Release, December 2012
Poess, M., Nambiar, R.O.: Energy cost, the key challenge of today’s data centers: a power consumption analysis of TPC-C results. PVLDB 1(2), 1229–1240 (2008)
Abadi, D., Agrawal, R., Ailamaki, A., Balazinska, M., Bernstein, P.A., Carey, M.J., Chaudhuri, S., Dean, J., Doan, A., Franklin, M.J., et al.: The beckman report on database research. Commun. ACM 59(2), 92–99 (2016)
Lang, W., Harizopoulos, S., Patel, J.M., Shah, M.A., Tsirogiannis, D.: Towards energy-efficient database cluster design. PVLDB 5(11), 1684–1695 (2012)
Li, X., Zhao, Y., Li, Y., Ju, L., Jia, Z.: An improved energy-efficient scheduling for precedence constrained tasks in multiprocessor clusters. In: Sun, X., Qu, W., Stojmenovic, I., Zhou, W., Li, Z., Guo, H., Min, G., Yang, T., Wu, Y., Liu, L. (eds.) ICA3PP 2014. LNCS, vol. 8630, pp. 323–337. Springer, Heidelberg (2014). doi:10.1007/978-3-319-11197-1_25
Boukorca, A., Bellatreche, L., Benkrid, S.: HYPAD: hyper-graph-driven approach for parallel data warehouse design. In: Wang, G., Zomaya, A., Perez, G.M., Li, K. (eds.) ICA3PP 2015. LNCS, vol. 9531, pp. 770–783. Springer, Heidelberg (2015). doi:10.1007/978-3-319-27140-8_53
Xu, Z., Tu, Y.C., Wang, X.: Dynamic energy estimation of query plans in database systems. In: ICDCS, pp. 83–92. IEEE (2013)
Xu, Z., Tu, Y.C., Wang, X.: Exploring power-performance tradeoffs in database systems. In: ICDE, pp. 485–496 (2010)
Kunjir, M., Birwa, P.K., Haritsa, J.R.: Peak power plays in database engines. In: EDBT, pp. 444–455. ACM (2012)
Lang, W., Kandhan, R., Patel, J.M.: Rethinking query processing for energy efficiency: slowing down to win the race. IEEE Data Eng. Bull. 34(1), 12–23 (2011)
Rodriguez-Martinez, M., Valdivia, H., et al.: Estimating power/energy consumption in database servers. Procedia Comput. Sci. 6, 112–117 (2011)
Roukh, A., Bellatreche, L.: Eco-processing of OLAP complex queries. In: Madria, S., Hara, T. (eds.) DaWaK 2015. LNCS, vol. 9263, pp. 229–242. Springer, Heidelberg (2015). doi:10.1007/978-3-319-22729-0_18
Lang, W., Patel, J.: Towards eco-friendly database management systems. arXiv preprint arXiv:0909.1767 (2009)
Roukh, A., Bellatreche, L., Boukorca, A., Bouarar, S.: Eco-dmw: eco-design methodology for data warehouses. In: DOLAP, pp. 1–10. ACM (2015)
Chaudhuri, S., Narasayya, V., Ramamurthy, R.: Estimating progress of execution for SQL queries. In: ACM SIGMOD, pp. 803–814. ACM (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Roukh, A., Bellatreche, L., Tziritas, N., Ordonez, C. (2016). Energy-Aware Query Processing on a Parallel Database Cluster Node. In: Carretero, J., Garcia-Blas, J., Ko, R., Mueller, P., Nakano, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2016. Lecture Notes in Computer Science(), vol 10048. Springer, Cham. https://doi.org/10.1007/978-3-319-49583-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-49583-5_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-49582-8
Online ISBN: 978-3-319-49583-5
eBook Packages: Computer ScienceComputer Science (R0)