Characterization of Different User Behaviors for Demand Response in Data Centers | SpringerLink
Skip to main content
Springer Nature Link
Log in

Characterization of Different User Behaviors for Demand Response in Data Centers

  • Conference paper
  • First Online:
Euro-Par 2022: Parallel Processing (Euro-Par 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13440))

Included in the following conference series:

  • 1424 Accesses

Abstract

Digital technologies are becoming ubiquitous while their impact increases. A growing part of this impact happens far away from the end users, in networks or data centers, contributing to a rebound effect. A solution for a more responsible use is therefore to involve the user. As a first step in this quest, this work considers the users of a data center and characterizes their contribution to curtail the computing load for a short period of time by solely changing their job submission behavior.

The contributions are: (i) an open-source plugin for the simulator Batsim to simulate users based on real data; (ii) the exploration of four types of user behaviors to curtail the load during a time window, namely delaying, degrading, reconfiguring or renouncing their job submissions. We study the impact of these behaviors on four different metrics: the energy consumed during and after the time window, the mean waiting time and the mean slowdown. We also characterize the conditions under which the involvement of users is the most beneficial.

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

Access this chapter

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

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 8007
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 10009
Price includes VAT (Japan)
  • 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

Similar content being viewed by others

Notes

  1. 1.

    Batsim: https://batsim.org/.

  2. 2.

    SimGrid: https://simgrid.org with the energy plugin https://simgrid.org/doc/latest/Plugins.html?highlight=energy#host-energy.

  3. 3.

    Batmen repository: https://gitlab.irit.fr/sepia-pub/mael/batmen.

  4. 4.

    Experiments repository: https://gitlab.irit.fr/sepia-pub/open-science/demand-response-user/-/tree/europar2022.

  5. 5.

    METACENTRUM-2013-3.swf available at https://www.cs.huji.ac.il/labs/parallel/workload/l_metacentrum2/index.html.

References

  1. Amokrane, A., Langar, R., Zhani, M.F., Boutaba, R., Pujolle, G.: Greenslater: on satisfying green SLAs in distributed clouds. IEEE Trans. Netw. Serv. Manag. 12(3), 363–376 (2015). https://doi.org/10.1109/TNSM.2015.2440423

    Article  Google Scholar 

  2. Basmadjian, R., Botero, J.F., Giuliani, G., Hesselbach, X., Klingert, S., De Meer, H.: Making data centers fit for demand response: introducing GreenSDA and GreenSLA contracts. IEEE Trans. Smart Grid 9(4), 3453–3464 (2018). https://doi.org/10.1109/TSG.2016.2632526

    Article  Google Scholar 

  3. Dupont, B., Mejri, N., Da Costa, G.: Energy-aware scheduling of malleable HPC applications using a particle swarm optimised greedy algorithm. Sustain. Comput. Inf. Syst. 28, 100447 (2020). https://doi.org/10.1016/j.suscom.2020.100447

    Article  Google Scholar 

  4. Dutot, P.-F., Mercier, M., Poquet, M., Richard, O.: Batsim: a realistic language-independent resources and jobs management systems simulator. In: Desai, N., Cirne, W. (eds.) JSSPP 2015-2016. LNCS, vol. 10353, pp. 178–197. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61756-5_10

    Chapter  Google Scholar 

  5. Feitelson, D.G.: Resampling with feedback — a new paradigm of using workload data for performance evaluation. In: Dutot, P.-F., Trystram, D. (eds.) Euro-Par 2016. LNCS, vol. 9833, pp. 3–21. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43659-3_1

    Chapter  Google Scholar 

  6. Freitag, C., Berners-Lee, M., Widdicks, K., Knowles, B., Blair, G.S., Friday, A.: The real climate and transformative impact of ICT: a critique of estimates, trends, and regulations. Patterns 2(9), 100340 (2021). https://doi.org/10.1016/j.patter.2021.100340

  7. Garg, S.K., Yeo, C.S., Buyya, R.: Green cloud framework for improving carbon efficiency of clouds. In: Jeannot, E., Namyst, R., Roman, J. (eds.) Euro-Par 2011. LNCS, vol. 6852, pp. 491–502. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23400-2_45

    Chapter  Google Scholar 

  8. Guyon, D., Orgerie, A.C., Morin, C.: Energy - efficient IaaS-PaaS co-design for flexible cloud deployment of scientific applications. In: 2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), pp. 69–76, September 2018. https://doi.org/10.1109/CAHPC.2018.8645888

  9. Guyon, D., Orgerie, A.C., Morin, C., Agarwal, D.: Involving users in energy conservation: a case study in scientific clouds. Int. J. Grid Util. Comput. 10(3), 272–282 (2019). https://doi.org/10.1504/IJGUC.2019.099667

    Article  Google Scholar 

  10. Haque, M.E., Le, K., Goiri, Í., Bianchini, R., Nguyen, T.D.: Providing green SLAs in high performance computing clouds. In: 2013 International Green Computing Conference Proceedings, pp. 1–11, June 2013. https://doi.org/10.1109/IGCC.2013.6604503

  11. Hilty, L.: Computing efficiency, sufficiency, and self-sufficiency: a model for sustainability? In: LIMITS 2015, First Workshop on Computing within Limits. s.n., Irvine, CA, USA, June 2015. https://doi.org/10.5167/uzh-110766

  12. Klusáček, D., Tóth, Š, Podolníková, G.: Real-life experience with major reconfiguration of job scheduling system. In: Desai, N., Cirne, W. (eds.) JSSPP 2015-2016. LNCS, vol. 10353, pp. 83–101. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61756-5_5

    Chapter  Google Scholar 

  13. Lannelongue, L., Grealey, J., Inouye, M.: Green algorithms: quantifying the carbon footprint of computation. Adv. Sci. 8(12), 2100707 (2021). https://doi.org/10.1002/advs.202100707

    Article  Google Scholar 

  14. Liu, Z., Wierman, A., Chen, Y., Razon, B., Chen, N.: Data center demand response: avoiding the coincident peak via workload shifting and local generation. Perform. Eval. 70(10), 770–791 (2013). https://doi.org/10.1016/j.peva.2013.08.014

    Article  Google Scholar 

  15. Madon, M., Da Costa, G., Pierson, J.M.: Artifact and instructions to generate experimental results for Euro-Par’2022 paper: characterization of different user behaviors for demand response in data centers, June 2022. https://doi.org/10.6084/m9.figshare.19948352

  16. Orgerie, A., Lefèvre, L., Gelas, J.: Save watts in your grid: green strategies for energy-aware framework in large scale distributed systems. In: 2008 14th IEEE International Conference on Parallel and Distributed Systems, pp. 171–178, December 2008. https://doi.org/10.1109/ICPADS.2008.97

  17. Wierman, A., Liu, Z., Liu, I., Mohsenian-Rad, H.: Opportunities and challenges for data center demand response. In: International Green Computing Conference, pp. 1–10, November 2014. https://doi.org/10.1109/IGCC.2014.7039172

  18. Zarnikau, J., Thal, D.: The response of large industrial energy consumers to four coincident peak (4CP) transmission charges in the Texas (ERCOT) market. Utilities Policy 26, 1–6 (2013). https://doi.org/10.1016/j.jup.2013.04.004

    Article  Google Scholar 

Download references

Acknowledgements and Data Availability Statement.

Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr). The scripts and instructions necessary to reproduce and analyze our result are available in a Figshare repository [15].

This work was partly supported by the French Research Agency under the project Energumen (ANR-18-CE25-0008) and DataZero2 (ANR-19-CE25-0016).

Author information

Authors and Affiliations

  1. IRIT, Université de Toulouse, CNRS, Toulouse INP, UT3, Toulouse, France

    Maël Madon, Georges Da Costa & Jean-Marc Pierson

Authors
  1. Maël Madon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Georges Da Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jean-Marc Pierson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maël Madon .

Editor information

Editors and Affiliations

  1. University of Glasgow, Glasgow, UK

    José Cano

  2. University of Glasgow, Glasgow, UK

    Phil Trinder

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Madon, M., Da Costa, G., Pierson, JM. (2022). Characterization of Different User Behaviors for Demand Response in Data Centers. In: Cano, J., Trinder, P. (eds) Euro-Par 2022: Parallel Processing. Euro-Par 2022. Lecture Notes in Computer Science, vol 13440. Springer, Cham. https://doi.org/10.1007/978-3-031-12597-3_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-12597-3_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12596-6

  • Online ISBN: 978-3-031-12597-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation