Energy-Proportional Query Processing on Database Clusters

  • Jiazhuang Xie
  • Peiquan JinEmail author
  • Shouhong Wan
  • Lihua Yue
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9098)


Energy efficiency has been a critical issue in database clusters. In this paper, we present an energy-proportional database cluster and propose an energy-proportional query processing approach to reduce the energy consumed by database clusters while keeping high time performance. Particularly, we introduce a query stream buffer on top of a database cluster and propose an unbalanced load allocation algorithm to distribute workloads among the cluster so as to realize better energy proportionality. Further, we present an adaptive algorithm to turn on/off nodes according to workload changes. With this mechanism, we can reduce energy consumption while keeping high time performance for query processing on database clusters. We build a prototype database cluster and use the TPC-H benchmark to compare our proposal with three baseline methods, where different query patterns are used. The results suggest the superiority of our proposal in energy savings and time performance.


Energy proportionality Database cluster Query processing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Schall, D., Hudlet, V.: WattDB: an energy - proportional cluster of wimpy nodes. In: Proc. of SIGMOD, pp. 1229–1232 (2011)Google Scholar
  2. 2.
    Jin, Y., Xing, B., Jin, P.: Towards a benchmark platform for measuring the energy consumption of database systems. In: Proc. of DTA, pp. 385–389 (2013)Google Scholar
  3. 3.
    Barroso, L., Hölzle, U.: The case for energy-proportional computing. IEEE Computer 40(12), 33–37 (2007)CrossRefGoogle Scholar
  4. 4.
    Lang, W., Patel, J.M.: Towards eco-friendly database management systems. In: CIDR (2009)Google Scholar
  5. 5.
    Graefe, G.: Database servers tailored to improve energy efficiency. In: Proc. of EDBT Workshop SETDM, pp 24–28 (2008)Google Scholar
  6. 6.
    Yang, P., Jin, P., Yue, L.: Exploiting the performance-energy tradeoffs for mobile database applications. Journal of Universal Computer Science 20(10), 1488–1498 (2014)Google Scholar
  7. 7.
    Wang, X., Liu, X., Fan, L., Huang, J.: Energy-aware resource management and green energy use for large-scale datacenters: a survey. In: Patnaik, S., Li, X. (eds.) Proceedings of International Conference on Computer Science and Information Technology. Advances in Intelligent Systems and Computing, vol. 255, pp. 555–563. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  8. 8.
    Lang, W., Harizopoulos, S., Patel, J., et al.: Towards energy-efficient database cluster design. Proceedings of the VLDB Endowment 5(11), 1684–1695 (2012)CrossRefGoogle Scholar
  9. 9.
    Nathuji, R., Kansal, A., Ghaffarkhah, A.: Q-clouds: managing performance interference effects for QoS-aware clouds. In: Proc. of EuroSys, pp. 237–250 (2010)Google Scholar
  10. 10.
    Leite, J., Kusic, D., Mossé, D., et al.: Stochastic approximation control of power and tardiness in a three-tier web-hosting cluster. In: Proc. of ICAC, pp 41–50 (2010)Google Scholar
  11. 11.
    Krioukov, A., Mohan, P., Alspaugh, S., et al.: Napsac: design and implementation of a power-proportional web cluster. Computer Communication Review 41(1), 102–108 (2011)CrossRefGoogle Scholar
  12. 12.
    Horvath, T., Skadron, K.: Multi-mode energy management for multi-tier server clusters. In: Proc. of PACT, pp. 270–279 (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jiazhuang Xie
    • 1
  • Peiquan Jin
    • 1
    • 2
    Email author
  • Shouhong Wan
    • 1
    • 2
  • Lihua Yue
    • 1
    • 2
  1. 1.School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina
  2. 2.Key Laboratory of Electromagnetic Space InformationChinese Academy of SciencesHefeiChina

Personalised recommendations