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IT Optimization for Datacenters Under Renewable Power Constraint

  • Stephane Caux
  • Paul Renaud-Goud
  • Gustavo Rostirolla
  • Patricia Stolf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11014)

Abstract

Nowadays, datacenters are one of the most energy consuming facilities due to the increase of cloud, web-services and high performance computing demands all over the world. To be clean and to be with no connection to the grid, datacenters projects try to feed electricity with renewable energy sources and storage elements. Nevertheless, due to the intermittent nature of these power sources, most of the works still rely on grid as a backup. This paper presents a model that considers the datacenter workload and the several moments where renewable energy could be engaged by the power side without grid. We propose to optimize the IT scheduling to execute tasks within a given power envelope of only renewable energy as a constraint.

Keywords

Cloud computing Renewable energy Scheduling 

Notes

Acknowledgments

The work presented in this paper was supported by the French ANR DATAZERO project ANR-15-CE25-0012. For source characterization, the experimental database has been obtained thanks to the financial support of several LAPLACE projects, France (leaders Christophe TURPIN, Eric BRU)

References

  1. 1.
    Khan, Z., Kiani, S.: A cloud-based architecture for citizen services in smart cities. In: 2012 IEEE Fifth International Conference on Utility and Cloud Computing (UCC), pp. 315–320, November 2012Google Scholar
  2. 2.
    Le, K., Bilgir, O., Bianchini, R., Martonosi, M., Nguyen, T.D.: Managing the cost, energy consumption, and carbon footprint of internet services. SIGMETRICS Perform. Eval. Rev. 38(1), 357–358 (2010)CrossRefGoogle Scholar
  3. 3.
    Koomey, J.: Growth in data center electricity use 2005 to 2010. In: A report by Analytical Press, completed at the request of The New York Times, p. 9 (2011)Google Scholar
  4. 4.
    Beldiceanu, N., et al.: Towards energy-proportional clouds partially powered by renewable energy. Computing 99(1), 3–22 (2017)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Orgerie, A.-C., Assuncao, M.D.D., Lefevre, L.: A survey on techniques for improving the energy efficiency of large-scale distributed systems. ACM Comput. Surv. 46(4), 1–31 (2014)CrossRefGoogle Scholar
  6. 6.
    Borgetto, D., Stolf, P.: An energy efficient approach to virtual machines management in cloud computing. In: 2014 IEEE 3rd International Conference on Cloud Networking (CloudNet), pp. 229–235, October 2014Google Scholar
  7. 7.
    Deng, W., Liu, F., Jin, H., Li, B., Li, D.: Harnessing renewable energy in cloud datacenters: opportunities and challenges. IEEE Netw. 28(1), 48–55 (2014)CrossRefGoogle Scholar
  8. 8.
    Goiri, I., et al.: GreenSlot scheduling energy consumption in green datacenters. In: 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC), pp. 1–11, November 2011Google Scholar
  9. 9.
    Goiri, I., Le, K., Nguyen, T.D., Guitart, J., Torres, J., Bianchini, R.: GreenHadoop: leveraging green energy in data-processing frameworks. In: Proceedings of the 7th ACM European Conference on Computer Systems, EuroSys 2012, pp. 57–70. ACM, New York (2012)Google Scholar
  10. 10.
    Aksanli, B., Venkatesh, J., Zhang, L., Rosing, T.: Utilizing green energy prediction to schedule mixed batch and service jobs in data centers. In: Proceedings of the 4th Workshop on Power-Aware Computing and Systems, HotPower 2011, pp. 5:1–5:5. ACM, New York (2011)Google Scholar
  11. 11.
    Liu, Z., Lin, M., Wierman, A., Low, S.H., Andrew, L.L.: Geographical load balancing with renewables. SIGMETRICS Perform. Eval. Rev. 39(3), 62–66 (2011)CrossRefGoogle Scholar
  12. 12.
    Sharma, N., Barker, S., Irwin, D., Shenoy, P.: Blink: managing server clusters on intermittent power. SIGARCH Comput. Archit. News 39(1), 185–198 (2011)CrossRefGoogle Scholar
  13. 13.
    Mudge, T.: Power: a first-class architectural design constraint. Computer 34, 52–58 (2001)CrossRefGoogle Scholar
  14. 14.
    Villebonnet, V., Costa, G.D., Lefevre, L., Pierson, J.M., Stolf, P.: Energy aware dynamic provisioning for heterogeneous data centers. In: 2016 28th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), pp. 206–213, October 2016Google Scholar
  15. 15.
    Da Costa, G., Grange, L., Courchelle, I.D.: Modeling and generating large-scale Google-like workload. In: 2016 Seventh International Green and Sustainable Computing Conference (IGSC), pp. 1–7, November 2016Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Stephane Caux
    • 1
  • Paul Renaud-Goud
    • 2
  • Gustavo Rostirolla
    • 1
    • 2
  • Patricia Stolf
    • 2
  1. 1.LAPLACE, Université de Toulouse, CNRSToulouseFrance
  2. 2.IRIT, Université de ToulouseToulouseFrance

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