Skip to main content
Springer Nature Link
Log in

An energy-efficient power management for heterogeneous servers in data centers

  • Regular Paper
  • Published:
Computing Aims and scope Submit manuscript

Abstract

Power management for heterogeneous servers has been playing a key role in improving energy efficiency in data centers. Running latency-critical web services on such scenario is still challenging due to the overheads of task transition between such servers. In this paper, we present a runtime power management system, Montgolfier, which is built on a latency-aware feedback control mechanism. It consolidates wimpy and brawny servers into composite nodes performing latency-critical applications to improve overall energy efficiency while ensuring QoS. The key idea behind Montgolfier is to mitigate the negative effect of server switches by dynamic load prediction and to determine thin-provisioned configurations in fine-grain manner within servers for daily fluctuating loads. Our evaluation results show that Montgolfier reduces energy consumption by up to 34.9% without violating any QoS constraints.

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
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

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
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Barroso LA, Clidaras J, Hölzle U (2013) The datacenter as a computer: an introduction to the Design of Warehouse-Scale Machines, 2nd edn. Morgan and Claypool Publishers, San Rafael

    Google Scholar 

  2. Mars J, Tang L (2013) Whare-map: heterogeneity in homogeneous warehouse-scale computers. In: Proceedings of the international symposium on computer architecture (ISCA). ACM, pp 619–630

  3. Delimitrou C, Kozyrakis C (2013) Paragon: QoS-aware scheduling for heterogeneous datacenters. In: Proceedings of the international conference on architectural support for programming languages and operating systems (ASPLOS). ACM, pp 77–88

  4. Petrucci V, Laurenzano MA et al (2015) Octopus-Man: QoS-driven task management for heterogeneous multicores in warehouse-scale computers. In: Proceedings of the high performance computer architecture (HPCA). IEEE, pp 246–258

  5. Reddi VJ, Lee BC et al (2010) Web search using mobile cores: quantifying and mitigating the price of efficiency. In: Proceedings of the international symposium on computer architecture (ISCA). ACM, pp 314–325

  6. Lotfi-Kamran P, Grot B et al (2012) Scale-out processors. In: Proceedings of the 39th annual international symposium on computer architecture (ISCA). IEEE, pp 500–511

  7. Cong J, Yuan B (2012) Energy-efficient scheduling on heterogeneous multi-core architectures. In: Proceedings of the international symposium on low power electronics and design (ISLPED). ACM, pp 345–350

  8. Koufaty D, Reddy D, Hahn S (2010) Bias scheduling in heterogeneous multi-core architectures. In: Proceedings of the European conference on computer systems (EuroSys). ACM, pp 125–138

  9. Li T, Brett P et al (2010) Operating system support for overlapping-ISA heterogeneous multi-core architectures. In: Proceedings of the high performance computer architecture (HPCA). IEEE, pp 1–12

  10. Chitlur N, Srinivasa G et al (2012) QuickIA: exploring heterogeneous architectures on real prototypes. In: Proceedings of the high performance computer architecture (HPCA). IEEE, p 1C8

  11. Greenhalgh P (2011) Big.LITTLE processing with ARM \(\text{Cortex}^{TM}\)-A15 and Cortex-A7. White paper ARM, pp 1–8

  12. Hölzle U (2010) Brawny cores still beat wimpy cores, most of the time. IEEE Micro 30(4):6–7

    Article  Google Scholar 

  13. Barroso LA, Hölzle U (2007) The case for energy-proportional computing. Computer 40:33–37

    Article  Google Scholar 

  14. Meisner D, Gold BT, Wenisch TF (2009) PowerNap: eliminating server idle power. In: Proceedings of the international conference on architectural support for programming languages and operating systems (ASPLOS). ACM, pp 205–216

  15. Wong D, Annavaram M (2012) Knightshift: scaling the energy proportionality wall through server-level heterogeneity. In: Proceedings of the 2012 45th annual IEEE/ACM international symposium on microarchitecture (MICRO). IEEE/ACM, pp 119–130

  16. Wong D, Annavaram M (2014) Implications of high energy proportional servers on cluster-wide energy proportionality. In: Proceedings of the high performance computer architecture (HPCA). IEEE, pp 142–153

  17. Rajamani K, Rawson F et al (2010) Power-performance management on an IBM POWER7 server. In: Proceedings of the 16th international symposium on low power electronics and design. ACM/IEEE, pp 201–206

  18. Leverich J, Monchiero M et al (2009) Power management of datacenter workloads using Per-Core power gating. IEEE Comput Archit Lett 8:48–51

    Article  Google Scholar 

  19. Hanumaiah V, Vrudhula S, Chatha KS (2011) Performance optimal online DVFS and task migration techniques for thermally constrained multi-core processors. IEEE Trans Comput Aided Des Integr Circuits Syst 30:1677–1690

    Article  Google Scholar 

  20. Lee J, Kim NS (2009) Optimizing throughput of power-and thermal-constrained multicore processors using DVFS and per-core power-gating. In: Proceedings of the design automation conference (DAC). ACM, pp 47–50

  21. Lo D, Cheng L et al (2014) Towards energy proportionality for large-scale latency-critical workloads. In: Proceedings of the international symposium on computer architecture (ISCA). ACM, pp 301–312

  22. Wu Q, Deng Q et al (2016) Dynamo: Facebook’s data center-wide power management system. In: Proceedings of the international symposium on computer architecture (ISCA). ACM, pp 469–480

  23. Dean J, Barroso LA (2013) The tail at scale. Commun ACM 56(2):74–80

    Article  Google Scholar 

  24. SPECJBB 2013: Java business benchmark. http://www.spec.org/jbb2013/

  25. Fan X, Weber WD, Barroso LA (2007) Power provisioning for a warehouse-sized computer. In: Proceedings of the international symposium on computer architecture (ISCA). ACM, pp 13–23

  26. Singh R, Irwin D et al (2013) Yank: enabling green data centers to pull the plug. In: Proceedings of networked systems design and implementation (NSDI). USENIX, pp 143–156

  27. Wang D, Govindan S et al (2014) Underprovisioning backup power infrastructure for datacenters. In: Proceedings of the international conference on architectural support for programming languages and operating systems (ASPLOS). ACM, pp 177–192

  28. Li C, Feng D et al (2017) BAC: bandwidth-aware compression for efficient live migration of virtual machines. In: Proceedings of the international conference on computer communications (INFOCOM). IEEE, pp 1–9

  29. Ruprecht A, Jones D et al (2018) VM live migration at scale. In: Proceedings of the international conference on virtual execution environments (VEE). ACM, pp 45–56

  30. Horvath T, Abdelzaher TT et al (2007) Dynamic voltage scaling in multitier web servers with end-to-end delay control. IEEE Trans Comput 56:444–458

    Article  MathSciNet  Google Scholar 

  31. Cai H, Cao Q et al (2016) Montgolfier: latency-aware power management system for heterogeneous servers. In: Proceedings of the IEEE 35th international performance computing and communications conference (IPCCC). IEEE, pp 1–8

  32. Tang L, Mars J et al (2011) The impact of memory subsystem resource sharing on datacenter applications. In: Proceedings of the international symposium on computer architecture (ISCA). ACM, pp 283–294

  33. Mars J, Tang L et al (2011) Bubble-up: increasing utilization in modern warehouse scale computers via sensible co-locations. In: Proceedings of the 44th annual IEEE/ACM international symposium on microarchitecture (MICRO). IEEE/ACM, pp 248–259

  34. Wang W, Dey T, Mars J et al (2012) Performance analysis of thread mappings with a holistic view of the hardware resources. In: Proceedings of the international symposium on performance analysis of systems and software (ISPASS). IEEE, pp 156–167

  35. Zhuravlev S, Blagodurov S, Fedorova A(2010) Addressing shared resource contention in multicore processors via scheduling. In: Proceedings of the international conference on architectural support for programming languages and operating systems (ASPLOS). ACM, pp 129–142

  36. Yang H, Breslow A et al (2013) Bubble-flux: precise online QoS management for increased utilization in warehouse scale computers. In: Proceedings of the international symposium on computer architecture (ISCA). ACM, pp 607–618

  37. Zh-101 portable electric power fault recorder and analyzer (2009)

  38. Ferdman M, Adileh A et al (2012) Clearing the clouds: a study of emerging scaleout workloads on modern hardware. In: Proceedings of the international conference on architectural support for programming languages and operating systems (ASPLOS). ACM, pp 37–48

  39. Daniel W, Murali A. Implications of high energy proportional servers on cluster-wide energy proportionality. In: Proceedings of the high performance computer architecture (HPCA). IEEE, pp 142–153 (2014)

  40. Bienia C, Kumar S et al (2008) The PARSEC benchmark suite: characterization and architectural implications. In: Proceedings of the 17th international conference on parallel architectures and compilation techniques (PACT). ACM, pp 72–81

Download references

Acknowledgements

We are grateful to the reviewers for their insightful comments and feedback. The work was partly supported by National Defense Preliminary Research Project (31511010202), NSFC No. 61832020, No. 61821003, Natural Science Foundation of Shandong Province (No. ZR2019LZH012). It was also supported by State Key Laboratory of High-end Server & Storage Technology. Qiang Wang and Haoran Cai contribute equally to this work.

Author information

Author notes

  1. Qiang Wang and Haoran Cai contribute equally to this work.

Authors and Affiliations

  1. Wuhan National Laboratory for Optoelectronics, Key Laboratory of Information Storage System, Ministry of Education of China, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China

    Qiang Wang, Haoran Cai, Qiang Cao & Fang Wang

Authors
  1. Qiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haoran Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qiang Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fang Wang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Q., Cai, H., Cao, Q. et al. An energy-efficient power management for heterogeneous servers in data centers. Computing 102, 1717–1741 (2020). https://doi.org/10.1007/s00607-020-00805-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-020-00805-w

Keywords

Mathematics Subject Classification