Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

CPicker: Leveraging Performance-Equivalent Configurations to Improve Data Center Energy Efficiency

  • 58 Accesses


The poor energy proportionality of server is seen as the principal source for low energy efficiency of modern data centers. We find that different resource configurations of an application lead to similar performance, but have distinct energy consumption. We call this phenomenon as “performance-equivalent resource configurations (PERC)”, and its performance range is called equivalent region (ER). Based on PERC, one basic idea for improving energy efficiency is to select the most efficient configuration from PERC for each application. However, it cannot support every application to obtain optimal solution when thousands of applications are run simultaneously on resource-bounded servers. Here we propose a heuristic scheme, CPicker, based on genetic programming to improve energy efficiency of servers. To speed up convergence, CPicker initializes a high quality population by first choosing configurations from regions that have high energy variation. Experiments show that CPicker obtains above 17% energy efficiency improvement compared with the greedy approach, and less than 4% efficiency loss compared with the oracle case.

This is a preview of subscription content, log in to check access.


  1. [1]

    Kirk D B, Strosnider J K. Smart (strategic memory allocation for real-time) cache design using the MIPS R3000. In Proc. the 11th Real-Time Systems Symp., December 1990, pp.322-330.

  2. [2]

    Ma J Y, Sui X F, Sun N H, Li Y P, Yu Z H, Huang B W, Xu T N, Yao Z C, Chen Y, Wang H B, Zhang L X, Bao Y G. Supporting differentiated services in computers via programmable architecture for resourcing-on-demand (PARD). In Proc. the 20th Int. Conf. Architectural Support for Programming Languages and Operating Systems, March 2015, pp.131-143.

  3. [3]

    Grandl R, Ananthanarayanan G, Kandula S, Rao S, Akella A. Multi-resource packing for cluster schedulers. In Proc. the 2014 ACM Conf. SIGCOMM, August 2014, pp.455-466.

  4. [4]

    Zaharia M, Chowdhury M, Franklin M J, Shenker S, Stoica I. Spark: Cluster computing with working sets. In Proc. the 2nd USENIX Conf. Hot Topics in Cloud Computing, June 2010.

  5. [5]

    Zaharia M, Chowdhury M, Das T, Dave A, Ma J, McCauley M, Franklin M J, Shenker S, Stoica I. Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In Proc. the 9th USENIX Conf. Networked Systems Design and Implementation, April 2012.

  6. [6]

    Lee S, Panigrahy R, Prabhakaran V, Ramasubramanian V, Talwar K, Uyeda L, Wieder U. Validating heuristics for virtual machines consolidation. https://www.microsoft.com/en-us/research/wp-content/uploads/2011/01/virtualization.pdf, July 2017.

  7. [7]

    Fréville A. The multidimensional 0–1 knapsack problem: An overview. European Journal of Operational Research, 2004, 155(1): 1-21.

  8. [8]

    Isci C, Buyuktosunoglu A, Cher C Y, Bose P, Martonosi M. An analysis of efficient multi-core global power management policies: Maximizing performance for a given power budget. In Proc. the 39th Annual IEEE/ACM Int. Symp. Microarchitecture , December 2006, pp.347-358.

  9. [9]

    Nathuji R, Schwan K. VirtualPower: Coordinated power management in virtualized enterprise systems. In Proc. the 21st ACM SIGOPS Symp. Operating Systems Principles, October 2007, pp.265-278.

  10. [10]

    Isci C, McIntosh S, Kephart J, Das R, Hanson J, Piper S, Wolford R, Brey T, Kantner R, Ng A, Norris J, Traore A, Frissora M. Agile, efficient virtualization power management with low-latency server power states. In Proc. the 40th Annual Int. Symp. Computer Architecture, June 2013, pp.96-107.

  11. [11]

    Lo D, Cheng L Q, Govindaraju R, Barroso L A, Kozyrakis C. Towards energy proportionality for large-scale latencycritical workloads. In Proc. the 41st Annual Int. Symp. Computer Architecuture, June 2014, pp.301-312.

  12. [12]

    Meisner D, Gold B T, Wenisch T F. PowerNap: Eliminating server idle power. In Proc. the 14th Int. Conf. Architectural Support for Programming Languages and Operating Systems, March 2009, pp.205-216.

  13. [13]

    Meisner D, Wenisch T F. DreamWeaver: Architectural support for deep sleep. In Proc. the 17th Int. Conf. Architectural Support for Programming Languages and Operating Systems, March 2012, pp.313-324.

  14. [14]

    Liu Y P, Draper S C, Kim N S. SleepScale: Runtime joint speed scaling and sleep states management for power efficient data centers. In Proc. the 41st Int. Symp. Computer Architecture (ISCA), June 2014, pp.313-324.

  15. [15]

    Liu F M, Zhou Z, Jin H, Li B, Li B C, Jiang H B. On arbitrating the power-performance tradeoff in SaaS clouds. IEEE Trans. Parallel and Distributed Systems, 2014, 25(10): 2648-2658.

  16. [16]

    Delimitrou C, Kozyrakis C. Quasar: Resource-efficient and QoS-aware cluster management. In Proc. the 19th Int. Conf. Architectural Support for Programming Languages and Operating Systems, March 2014, pp.127-144.

  17. [17]

    Delimitrou C, Kozyrakis C. Paragon: QoS-aware scheduling for heterogeneous datacenters. In Proc. the 18th Int. Conf. Architectural Support for Programming Languages and Operating Systems, March 2013, pp.77-88.

  18. [18]

    Lo D, Cheng L Q, Govindaraju R, Ranganathan P, Kozyrakis C. Heracles: Improving resource efficiency at scale. In Proc. the 42nd Annual Int. Symp. Computer Architecture, June 2015, pp.450-462.

  19. [19]

    Yang H L, Breslow A, Mars J, Tang L J. Bubble-flux: Precise online QoS management for increased utilization in warehouse scale computers. In Proc. the 40th Annual Int. Symp. Computer Architecture, June 2013, pp.607-618.

  20. [20]

    Beloglazov A, Buyya R. Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers. In Proc. the 8th Int. Workshop on Middleware for Grids, Clouds and e-Science, December 2010.

  21. [21]

    Salimian L, Safi F. Survey of energy efficient data centers in cloud computing. In Proc. the 6th Int. Conf. Utility and Cloud Computing, December 2013, pp.369-374.

  22. [22]

    Xu F, Liu F M, Liu L H, Jin H, Li B, Li B C. iAware: Making live migration of virtual machines interference-aware in the cloud. IEEE Trans. Computers, 2014, 63(12): 3012-3025.

  23. [23]

    Deng W, Liu F M, Jin H, Liao X F, Liu H K. Reliability-aware server consolidation for balancing energy-lifetime tradeoff in virtualized cloud datacenters. International Journal of Communication Systems, 2014, 27(4): 623-642.

  24. [24]

    Baset S A, Wang L, Tang C Q. Towards an understanding of oversubscription in cloud. In Proc. the 2nd USENIX Conf. Hot Topics in Management of Internet, Cloud, and Enterprise Networks and Services, April 2012.

  25. [25]

    Householder R, Arnold S, Green R. On cloud-based oversubscription. International Journal of Engineering Trends and Technology (IJETT), 2014, 8(8): 425-431.

  26. [26]

    Wang L, Zhan J F, Luo C J, Zhu Y Q, Yang Q, He Y Q, Gao W L, Jia Z, Shi Y J, Zhang S J, Zheng C, Lu G, Zhan K, Li X N, Qiu B Z. BigDataBench: A big data benchmark suite from Internet services. In Proc. the 20th Int. Symp. High Performance Computer Architecture (HPCA), February 2014, pp.488-499.

  27. [27]

    Yan G H, Ma J, Han Y H, Li X W. EcoUp: Towards economical datacenter upgrading. IEEE Trans. Parallel and Distributed Systems, 2016, 27(7): 1968-1981.

  28. [28]

    Chen T S, Guo Q, Temam O, Wu Y, Bao Y G, Xu Z W, Chen Y J. Statistical performance comparisons of computers. IEEE Trans. Computers, 2015, 64(5): 1442-1455.

  29. [29]

    Yan G H, Sun F Q, Li H W, Li XW. CoreRank: Redeeming “Sick Silicon” by dynamically quantifying core-level healthy condition. IEEE Trans. Computers, 2016, 65(3): 716-729.

  30. [30]

    Winter J A, Albonesi D H, Shoemaker C A. Scalable thread scheduling and global power management for heterogeneous many-core architectures. In Proc. the 19th Int. Conf. Parallel Architectures and Compilation Techniques, September 2010, pp.29-40.

  31. [31]

    Nia M B, Alipouri Y. Speeding up the genetic algorithm convergence using sequential mutation and circular gene methods. In Proc. the 9th Int. Conf. Intelligent Systems Design and Applications, December 2009, pp.31-36.

  32. [32]

    Kansal A, Zhao F, Liu J, Kothari N, Bhattacharya A A. Virtual machine power metering and provisioning. In Proc. the 1st ACM Symp. Cloud Computing, June 2010, pp.39-50.

Download references

Author information

Correspondence to Gui-Hai Yan or Hua-Wei Li.

Electronic supplementary material

Below is the link to the electronic supplementary material.


(PDF 385 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sun, F., Yan, G., He, X. et al. CPicker: Leveraging Performance-Equivalent Configurations to Improve Data Center Energy Efficiency. J. Comput. Sci. Technol. 33, 131–144 (2018). https://doi.org/10.1007/s11390-018-1811-x

Download citation


  • performance equivalence
  • energy efficiency
  • data center
  • power management
  • dynamic voltage and frequency scaling (DVFS)