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The SAGITTA Approach for Optimizing Solar Energy Consumption in Distributed Clouds with Stochastic Modeling

  • Benjamin Camus
  • Fanny Dufossé
  • Anne-Cécile OrgerieEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 921)

Abstract

Facing the urgent need to decrease data centers’ energy consumption, Cloud providers resort to on-site renewable energy production. Solar energy can thus be used to power data centers. Yet this energy production is intrinsically fluctuating over time and depending on the geographical location. In this paper, we propose a stochastic modeling for optimizing solar energy consumption in distributed clouds. Our approach, named SAGITTA (Stochastic Approach for Green consumption In disTributed daTA centers), is shown to produce a virtual machine scheduling close to the optimal algorithm in terms of energy savings and to outperform classical round-robin approaches over varying Cloud workloads and real solar energy generation traces.

Keywords

Data centers Distributed clouds Energy efficiency Renewable energy Scheduling On/Off techniques 

Notes

Acknowledgments

This work has been supported by the Inria exploratory research project COSMIC (Coordinated Optimization of SMart grIds and Clouds).

References

  1. 1.
    Shehabi, A., et al.: United States Data Center Energy Usage Report. Technical report, Lawrence Berkeley National Laboratory (2016)Google Scholar
  2. 2.
    Tripathi, R., Vignesh, S., Tamarapalli, V.: Optimizing green energy, cost, and availability in distributed data centers. IEEE Commun. Lett. 21(3), 500–503 (2017)CrossRefGoogle Scholar
  3. 3.
    Wang, D., Ren, C., Sivasubramaniam, A., Urgaonkar, B., Fathy, H.: Energy storage in datacenters: What, where, and how much? In: ACM SIGMETRICS/PERFORMANCE, pp. 187–198 (2012)CrossRefGoogle Scholar
  4. 4.
    Camus, B., Dufossé, F., Orgerie, A.C.: A stochastic approach for optimizing green energy consumption in distributed clouds. In: International Conference on Smart Cities and Green ICT Systems (SMARTGREENS), pp. 47–59 (2017)Google Scholar
  5. 5.
    Wang, L., Tao, J., Kunze, M., Castellanos, A., Kramer, D., Karl, W.: Scientific cloud computing: early definition and experience. In: IEEE International Conference on High Performance Computing and Communications (HPCC), pp. 825–830 (2008)Google Scholar
  6. 6.
    Koomey, J.: Growth in Data Center Electricity Use 2005 to 2010. Analytics Press, Berkeley (2011)Google Scholar
  7. 7.
    Katz, R.H.: Tech titans building boom. IEEE Spectr. 46, 40–54 (2009)CrossRefGoogle Scholar
  8. 8.
    : How dirty is your data? Greenpeace report (2011)Google Scholar
  9. 9.
    Figuerola, S., Lemay, M., Reijs, V., Savoie, M., St. Arnaud, B.: Converged optical network infrastructures in support of future internet and grid services using IaaS to reduce GHG emissions. J. Lightwave Technol. 27, 1941–1946 (2009)CrossRefGoogle Scholar
  10. 10.
    Callau-Zori, M., Samoila, L., Orgerie, A.C., Pierre, G.: An experiment-driven energy consumption model for virtual machine management systems. Technical Report 8844, Inria (2016)Google Scholar
  11. 11.
    Fan, X., Weber, W.D., Barroso, L.A.: Power provisioning for a warehouse-sized computer. In: ACM International Symposium on Computer Architecture (ISCA), pp. 13–23 (2007)Google Scholar
  12. 12.
    Snowdon, D., Ruocco, S., Heiser, G.: Power management and dynamic voltage scaling: myths and facts. In: Workshop on Power Aware Real-time Computing (2005)Google Scholar
  13. 13.
    Talaber, R., Brey, T., Lamers, L.: Using Virtualization to Improve Data Center Efficiency. Technical report, The Green Grid (2009)Google Scholar
  14. 14.
    Barham, P., et al.: Xen and the art of virtualization. In: ACM Symposium on Operating Systems Principles (SOSP), pp. 164–177 (2003)Google Scholar
  15. 15.
    Miyoshi, A., Lefurgy, C., Van Hensbergen, E., Rajamony, R., Rajkumar, R.: Critical power slope: understanding the runtime effects of frequency scaling. In: ACM International Conference on Supercomputing (ICS), pp. 35–44 (2002)Google Scholar
  16. 16.
    Orgerie, A.C., Dias de Assunção, M., Lefèvre, L.: A survey on techniques for improving the energy efficiency of large-scale distributed systems. ACM Comput. Surv. (CSUR) 46, 47:1–47:31 (2014)CrossRefGoogle Scholar
  17. 17.
    Raïs, I., Orgerie, A.-C., Quinson, M.: Impact of shutdown techniques for energy-efficient cloud data centers. In: Carretero, J., Garcia-Blas, J., Ko, R.K.L., Mueller, P., Nakano, K. (eds.) ICA3PP 2016. LNCS, vol. 10048, pp. 203–210. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-49583-5_15CrossRefGoogle Scholar
  18. 18.
    Ren, S., He, Y., Xu, F.: Provably-efficient job scheduling for energy and fairness in geographically distributed data centers. In: IEEE International Conference on Distributed Computing Systems (ICDCS), pp. 22–31 (2012)Google Scholar
  19. 19.
    Deng, W., Liu, F., Jin, H., Li, B., Li, D.: Harnessing renewable energy in cloud datacenters: opportunities and challenges. IEEE Netw. 28, 48–55 (2014)CrossRefGoogle Scholar
  20. 20.
    Tighe, M., Keller, G., Bauer, M., Lutfiyya, H.: DCSim: a data centre simulation tool for evaluating dynamic virtualized resource management. In: Workshop on Systems Virtualization Management, pp. 385–392 (2012)Google Scholar
  21. 21.
    Camus, B., et al.: Hybrid co-simulation of FMUs using DEV&DESS in MECSYCO. In: Symposium on Theory of Modeling & Simulation - DEVS Integrative M&S Symposium (2016)Google Scholar
  22. 22.
    Camus, B., et al.: MECSYCO: a multi-agent DEVS wrapping platform for the co-simulation of complex systems. Research report, LORIA (2016)Google Scholar
  23. 23.
    Zeigler, B., Praehofer, H., Kim, T.: Theory of modeling and simulation: integrating discrete event and continuous complex dynamic systems. Academic Press, Cambridge (2000)Google Scholar
  24. 24.
    Li, Y., Orgerie, A.C., Menaud, J.M.: Opportunistic scheduling in clouds partially powered by green energy. In: IEEE International Conference on Green Computing and Communications (GreenCom) (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Benjamin Camus
    • 1
  • Fanny Dufossé
    • 2
  • Anne-Cécile Orgerie
    • 3
    Email author
  1. 1.Inria, IRISARennesFrance
  2. 2.Inria, CRIStALLilleFrance
  3. 3.CNRS, IRISARennesFrance

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