LPC $$_\mathrm{FreqSchd}$$ : A local power controller using the frequency scheduling approach for virtualized servers | Cluster Computing Skip to main content
Springer Nature Link
Log in

LPC\(_\mathrm{FreqSchd}\): A local power controller using the frequency scheduling approach for virtualized servers

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

For more than a decade, the power consumption of data centers has been addressed from different perspectives. Many solutions have been proposed to reduce (or optimize) this power consumption, such as controlling the operation of the servers in data centers. However, these approaches have not yet reached their optimum goals. Existing power control solutions using CPU frequency with an ad hoc or frequency modulator approach are not sufficient. In this paper, we review the power consumption effects of different configuration settings applied to the server’s CPU. We propose our local power controller using frequency scheduling (LPC\(_\mathrm{FreqSchd}\)), which is a server-level power controller that depends on an extended gain scheduling technique. Our proposed LPC\(_\mathrm{FreqSchd}\) considers the impact of different CPU configuration settings that are typically not considered simultaneously, such as the allocated CPU credits and CPU frequency level. Through a real experimental test bed, our LPC\(_\mathrm{FreqSchd}\) exhibits effective power management of different types of machines and outperforms other existing approaches, such as ad hoc and frequency modulation, when the power budget is low. Moreover, our proposed LPC\(_\mathrm{FreqSchd}\) has a very lightweight control actuation overhead compared with other approaches: approximately \(1/10 \mathrm{th}\) of the ad hoc approach’s overhead and \(1/100 \mathrm{th}\) of the frequency modulator approach’s overhead. This lightweight control actuation overhead reduces the power consumption overhead caused by the controller, and it could be used by other controllers, such as performance or thermal controllers running on the same server.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Koomey, J.: Growth in data center electricity use 2005 to 2010. A report by Analytical Press, completed at the request of The New York Times p. 9 (2011)

  2. Koomey, J.G.: Worldwide electricity used in data centers. Environ. Res. Lett. 3(3), 034008 (2008)

    Article  Google Scholar 

  3. Koomey, J.G., Berard, S., Sanchez, M., Wong, H.: Implications of historical trends in the electrical efficiency of computing. IEEE Ann. Hist. Comput. 33(3), 46–54 (2011)

    Article  MathSciNet  Google Scholar 

  4. Deng, Q., Meisner, D., Bhattacharjee, A., Wenisch, T.F., Bianchini, R.: Coscale: Coordinating cpu and memory system dvfs in server systems. In: 45th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), pp. 143–154. IEEE, Piscataway (2012)

  5. Lefurgy, C., Wang, X., Ware, M.: Server-level power control. In: Fourth International Conference on Autonomic Computing, ICAC’07, pp. 4–4. IEEE, Piscataway (2007)

  6. Raghavendra, R., Ranganathan, P., Talwar, V., Wang, Z., Zhu, X.: No power struggles: Coordinated multi-level power management for the data center. In: ACM SIGARCH Computer Architecture News, vol. 36, pp. 48–59. ACM, New York (2008)

  7. Ardagna, D., Panicucci, B., Trubian, M., Zhang, L.: Energy-aware autonomic resource allocation in multitier virtualized environments. IEEE Trans. Serv. Comput. 5(1), 2–19 (2012)

    Article  Google Scholar 

  8. Chen, S., Joshi, K.R., Hiltunen, M.A., Schlichting, R.D., Sanders, W.H.: Blackbox prediction of the impact of dvfs on end-to-end performance of multitier systems. ACM SIGMETRICS Perform. Eval. Rev. 37(4), 59–63 (2010)

    Article  Google Scholar 

  9. Lu, G., Zhan, J., Wang, H., Yuan, L., Gao, Y., Weng, C., Qi, Y.: Powertracer: tracing requests in multi-tier services to reduce energy inefficiency. IEEE Trans. Comput. 64(5), 1389–1401 (2015)

    Article  MathSciNet  Google Scholar 

  10. Wang, X., Chen, M., Lefurgy, C., Keller, T.W.: Ship: A scalable hierarchical power control architecture for large-scale data centers. IEEE Trans. Parallel Distrib. Syst. 23(1), 168–176 (2012)

    Article  Google Scholar 

  11. Lama, P., Guo, Y., Jiang, C., Zhou, X.: Autonomic performance and power control for co-located web applications in virtualized datacenters. IEEE Trans. Parallel Distrib. Syst. pp. (99), 1–1 (2015). doi:10.1109/TPDS.2015.2453971

  12. Park, S.M., Humphrey, M.A.: Predictable high-performance computing using feedback control and admission control. IEEE Trans. Parallel Distrib. Syst. 22(3), 396–411 (2011)

    Article  Google Scholar 

  13. Wang, X., Du, Z., Chen, Y., Li, S.: Virtualization-based autonomic resource management for multi-tier web applications in shared data center. J. Syst. Softw. 81(9), 1591–1608 (2008)

    Article  Google Scholar 

  14. Al-Hazemi, F., Peng, Y., Youn, C.H.: A miso model for power consumption in virtualized servers. Clust. Comput. 18(2), 847–863 (2015)

    Article  Google Scholar 

  15. Mobius, C., Dargie, W., Schill, A.: Power consumption estimation models for processors, virtual machines, and servers. IEEE Trans. Parallel Distrib. Syst. 25(6), 1600–1614 (2014)

    Article  Google Scholar 

  16. Xen sched-credit. http://wiki.xen.org/wiki/Credit_Scheduler/. Accessed 25 June 2015

  17. Cpu usage limiter for linux. http://cpulimit.sourceforge.net/. Accessed 25 June 2015

  18. Linpack. http://www.netlib.org/linpack/. Accessed 25 June 2015

  19. Lefurgy, C., Wang, X., Ware, M.: Power capping: a prelude to power shifting. Clust. Comput. 11(2), 183–195 (2008)

    Article  Google Scholar 

  20. Leith, D.J., Leithead, W.E.: Survey of gain-scheduling analysis and design. Int. J. Control 73(11), 1001–1025 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  21. Rugh, W.J., Shamma, J.S.: Research on gain scheduling. Automatica 36(10), 1401–1425 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  22. Bennett, S.: A history of control engineering, 1930–1955, vol. 47. IET, London (1993)

    MATH  Google Scholar 

  23. Yoctopuce. http://www.yoctopuce.com/. Accessed 25 June 2015

  24. Delta-sigma modulator. https://en.wikipedia.org/wiki/Delta-sigma_modulation/. Accessed 25 June 2015

  25. AL-Hazemi, F.: Green polymorphic approach for service quality. In: IEEE 20th International Conference onSoftware, Telecommunications and Computer Networks (SoftCOM), pp. 1–6. (2012)

  26. Al-Hazemi, F.: Feedback green control for data centers autonomy. In: IEEE/ACM 6th International Conference on Utility and Cloud Computing (UCC), pp. 333–338. IEEE, Piscataway (2013)

  27. Al-Hazemi, F.: A hybrid green policy for admission control in web-based applications. In: 21st International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 1–6. IEEE, Piscataway (2013)

  28. Al-Hazemi, F.: A polymorphic green service approach for data center energy consumption management. In: IEEE International Conference on Green Computing and Communications (GreenCom), 2013 IEEE and Internet of Things (iThings/CPSCom), and IEEE Cyber, Physical and Social Computing, pp. 110–117. IEEE, Piscataway (2013)

  29. Al-Hazemi, F.: Temporal power model for effective usage in data center. In: IEEE International Conference on Green Computing and Communications (GreenCom), 2013 IEEE and Internet of Things (iThings/CPSCom), and IEEE Cyber, Physical and Social Computing, pp. 317–319. IEEE, Piscataway (2013)

  30. Cheng, D., Guo, Y., Jiang, C., Zhou, X.: Self-tuning batching with dvfs for performance improvement and energy efficiency in internet servers. ACM Trans. Auton. Adapt. Syst. (TAAS) 10(1), 6 (2015)

    Google Scholar 

  31. Wang, X., Chen, M., Fu, X.: Mimo power control for high-density servers in an enclosure. IEEE Trans. Parallel Distrib. Syst. 21(10), 1412–1426 (2010)

  32. Wang, X., Ma, K., Wang, Y.: Adaptive power control with online model estimation for chip multiprocessors. IEEE Trans. Parallel Distrib. Syst. 22(10), 1681–1696 (2011)

    Article  Google Scholar 

  33. Wang, X., Wang, Y.: Coordinating power control and performance management for virtualized server clusters. IEEE Trans. Parallel Distrib. Syst. 22(2), 245–259 (2011)

    Article  Google Scholar 

  34. Wang, Y., Wang, X.: Virtual batching: request batching for server energy conservation in virtualized data centers. IEEE Trans. Parallel Distrib. Syst. 24(8), 1695–1705 (2013)

    Article  Google Scholar 

  35. Wang, Y., Wang, X.: Performance-controlled server consolidation for virtualized data centers with multi-tier applications. Sustain. Comput. 4(1), 52–65 (2014)

    Google Scholar 

  36. Shi, X., Briere, C.A., Djouadi, S.M., Wang, Y., Feng, Y.: Power-aware performance management of virtualized enterprise servers via robust adaptive control. Clust. Comput. 18(1), 419–433 (2015)

    Article  Google Scholar 

  37. Guo, Y., Lama, P., Jiang, C., Zhou, X.: Automated and agile server parametertuning by coordinated learning and control. IEEE Trans. Parallel Distrib. Syst. 25(4), 876–886 (2014)

    Article  Google Scholar 

  38. Lama, P., Zhou, X.: Efficient server provisioning with control for end-to-end response time guarantee on multitier clusters. IEEE Trans. Parallel Distrib. Syst. 23(1), 78–86 (2012)

    Article  Google Scholar 

  39. Lama, P., Zhou, X.: Coordinated power and performance guarantee with fuzzy mimo control in virtualized server clusters. IEEE Trans. Comput. 64(1), 97–111 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  40. Patikirikorala, T., Wang, L., Colman, A., Han, J.: Differentiated performance management in virtualized environments using nonlinear control. IEEE Trans. Netw. Serv. Manag. 12(1), 101–113 (2015)

    Article  Google Scholar 

  41. Xu, J., Zhao, M., Fortes, J., Carpenter, R., Yousif, M.: Autonomic resource management in virtualized data centers using fuzzy logic-based approaches. Clust. Comput. 11(3), 213–227 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, KAIST, Bldg. E3-2, E6-3211, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, South Korea

    Fawaz AL-Hazemi, Dong-Ki Kang, Seong-Hwan Kim, Yuyang Peng, S. H. Shah Newaz & Chan-Hyun Youn

Authors
  1. Fawaz AL-Hazemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dong-Ki Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seong-Hwan Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuyang Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. S. H. Shah Newaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chan-Hyun Youn
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fawaz AL-Hazemi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

AL-Hazemi, F., Kang, DK., Kim, SH. et al. LPC\(_\mathrm{FreqSchd}\): A local power controller using the frequency scheduling approach for virtualized servers. Cluster Comput 19, 663–678 (2016). https://doi.org/10.1007/s10586-016-0562-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-016-0562-0

Keywords

Search

Navigation