Skip to main content
SpringerLink
Log in

Dynamic Management of CPU Resources Towards Energy Efficient and Profitable Datacentre Operation

  • Conference paper
  • First Online:
Job Scheduling Strategies for Parallel Processing (JSSPP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13592))

Included in the following conference series:

  • 262 Accesses

Abstract

Energy reduction has become a necessity for modern datacentres, with CPU being a key contributor to the energy consumption of nodes. Increasing the utilization of CPU resources on active nodes is a key step towards energy efficiency. However, this is a challenging undertaking, as the workload can vary significantly among the nodes and over time, exposing operators to the risk of overcommitting the CPU. In this paper, we explore the trade-off between energy efficiency and node overloads, to drive virtual machine (VM) consolidation in a cost-aware manner. We introduce a model that uses runtime information to estimate the target utilization of the nodes to control their load, identifying and considering correlated behavior among collocated workloads. Moreover, we introduce a VM allocation and node management policy that exploits the model to increase the profit of datacentre operators considering the trade-off between energy reduction and potential SLA violation costs. We evaluate our work through simulations using node profiles derived from real machines and workloads from real datacentre traces. The results show that our policy adapts the nodes’ target utilization in a highly effective way, converging to a target utilization that is statically optimal for the workload at hand. Moreover, we show that our policy closely matches, or even outperforms two state-of-the-art policies that combine VM consolidation with VFS – the second one, also operating the CPU at reduced voltage margins – even when these are configured to use a static, workload- and architecture-specific target utilization derived through offline characterization of the workload.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Amazon EC2 pricing. https://aws.amazon.com/ec2/pricing/

  2. Eletric Power Monthly. https://www.eia.gov/electricity/monthly/

  3. Arroba, P., Moya, J.M., Ayala, J.L., Buyya, R.: Dynamic Voltage and Frequency Scaling-aware dynamic consolidation of virtual machines for energy efficient cloud data centers. Concurrency Comput. Pract. Experience 29(10), e4067 (2017)

    Google Scholar 

  4. Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28(5), 755–768 (2012)

    Article  Google Scholar 

  5. Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurrency Comput. Pract. Experience 24(13), 1397–1420 (2012)

    Article  Google Scholar 

  6. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)

    Article  Google Scholar 

  7. Cao, Z., Dong, S.: An energy-aware heuristic framework for virtual machine consolidation in cloud computing. J. Supercomput. 69(1), 429–451 (2014)

    Article  Google Scholar 

  8. Dayarathna, M., Wen, Y., Fan, R.: Data center energy consumption modeling: a survey. IEEE Commun. Surv. Tutorials 18(1), 732–794 (2016)

    Article  Google Scholar 

  9. Engbers, N., Taen, E.: Green Data Net. Report to IT Room INFRA. European Commission. FP7 ICT 2013.6.2;2014 (2016)

    Google Scholar 

  10. Farahnakian, F., Liljeberg, P., Plosila, J.: Energy-efficient virtual machines consolidation in cloud data centers using reinforcement learning. In: 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, pp. 500–507, February 2014. https://doi.org/10.1109/PDP.2014.109

  11. Garg, S.K., Gopalaiyengar, S.K., Buyya, R.: SLA-based resource provisioning for heterogeneous workloads in a virtualized cloud datacenter. In: Xiang, Y., Cuzzocrea, A., Hobbs, M., Zhou, W. (eds.) ICA3PP 2011. LNCS, vol. 7016, pp. 371–384. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24650-0_32

    Chapter  Google Scholar 

  12. Ghribi, C., Hadji, M., Zeghlache, D.: Energy efficient VM scheduling for cloud data centers: exact allocation and migration algorithms. In: 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, pp. 671–678, May 2013. https://doi.org/10.1109/CCGrid.2013.89

  13. Herbert, S., Marculescu, D.: Analysis of dynamic voltage/frequency scaling in chip-multiprocessors. In: 2007 ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED), pp. 38–43, August 2007. https://doi.org/10.1145/1283780.1283790

  14. Iqbal, W., Dailey, M.N., Carrera, D., Janecek, P.: Adaptive resource provisioning for read intensive multi-tier applications in the cloud. Future Gener. Comput. Syst. 27(6), 871–879 (2011)

    Article  Google Scholar 

  15. Kalogirou, C., et al.: Exploiting CPU voltage margins to increase the profit of cloud infrastructure providers. In: 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pp. 302–311. IEEE (2019)

    Google Scholar 

  16. von Laszewski, G., Wang, L., Younge, A.J., He, X.: Power-aware scheduling of virtual machines in DVFS-enabled clusters. In: 2009 IEEE International Conference on Cluster Computing and Workshops, pp. 1–10, August 2009. https://doi.org/10.1109/CLUSTR.2009.5289182

  17. Lee, Y.C., Zomaya, A.Y.: Energy efficient utilization of resources in cloud computing systems. J. Supercomput. 60(2), 268–280 (2012)

    Article  Google Scholar 

  18. Liu, W., Du, W., Chen, J., Wang, W., Zeng, G.: Adaptive energy-efficient scheduling algorithm for parallel tasks on homogeneous clusters. J. Netw. Comput. Appl. 41, 101–113 (2014)

    Article  Google Scholar 

  19. Reiss, C., Wilkes, J., Hellerstein, J.L.: Google cluster-usage traces: format + schema. Technical report, Google Inc., Mountain View, CA, USA, November 2011. revised 2014–11-17 for version 2.1. Posted at https://github.com/google/cluster-data

  20. Salimian, L., Esfahani, F.S., Nadimi-Shahraki, M.H.: An adaptive fuzzy threshold-based approach for energy and performance efficient consolidation of virtual machines. Computing 98(6), 641–660 (2016)

    Article  MathSciNet  Google Scholar 

  21. Yadav, R., Zhang, W., Kaiwartya, O., Singh, P.R., Elgendy, I.A., Tian, Y.C.: Adaptive energy-aware algorithms for minimizing energy consumption and SLA violation in cloud computing. IEEE Access 6, 55923–55936 (2018)

    Article  Google Scholar 

  22. Zhou, Z., et al.: Minimizing SLA violation and power consumption in Cloud data centers using adaptive energy-aware algorithms. Future Gener. Comput. Syst. 86, 836–850 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Thessaly, Volos, Greece

    Christos Kalogirou, Christos D. Antonopoulos, Spyros Lalis & Nikolaos Bellas

Authors
  1. Christos Kalogirou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christos D. Antonopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Spyros Lalis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nikolaos Bellas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christos Kalogirou .

Editor information

Editors and Affiliations

  1. CESNET, Prague, Czech Republic

    Dalibor Klusáček

  2. Polytechnic University of Catalonia, Barcelona, Spain

    Corbalán Julita

  3. Apple, Cupertino, CA, USA

    Gonzalo P. Rodrigo

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kalogirou, C., Antonopoulos, C.D., Lalis, S., Bellas, N. (2023). Dynamic Management of CPU Resources Towards Energy Efficient and Profitable Datacentre Operation. In: Klusáček, D., Julita, C., Rodrigo, G.P. (eds) Job Scheduling Strategies for Parallel Processing. JSSPP 2022. Lecture Notes in Computer Science, vol 13592. Springer, Cham. https://doi.org/10.1007/978-3-031-22698-4_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-22698-4_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22697-7

  • Online ISBN: 978-3-031-22698-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation