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Scaling Energy Adaptive Applications for Sustainable Profitability

  • Fabien Hermenier
  • Giuliani Giovanni
  • Andre Milani
  • Sophie Demassey
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10417)

Abstract

Energy efficiency in data centres is addressed through workload management usually to reduce the operational costs and as a by-product, the environmental footprint. This includes to minimise total power consumption or to minimise the power issued from non-renewable energy sources. Hence, the performance requirements of the client’s applications are either totally overlooked or strictly enforced.

To encourage profitable sustainability in data centres, we consider the total financial gain as a trade-off between energy efficiency and client satisfaction. We propose Carver to orchestrate energy-adaptive applications, according to performance and environmental preferences and given forecasts of the renewable energy production. We validated Carver by simulating a testbed powered by the grid and a photovoltaic array and running the Web service HP LIFE.

References

  1. 1.
    Aksanli, B., Venkatesh, J., Zhang, L., Rosing, T.: Utilizing green energy prediction to schedule mixed batch and service jobs in data centers. SIGOPS OSR 45(3), 53–57 (2012)CrossRefGoogle Scholar
  2. 2.
    Cano, I., Aiyar, S., Krishnamurthy, A.: Characterizing private clouds: a large-scale empirical analysis of enterprise clusters. In: Proceedings of the Seventh ACM Symposium on Cloud Computing, SoCC 2016. ACM, New York (2016). http://doi.acm.org/10.1145/2987550.2987584
  3. 3.
    Colmant, M., Kurpicz, M., Felber, P., Huertas, L., Rouvoy, R., Sobe, A.: Process-level power estimation in VM-based systems. In: Proceeding of the Tenth EuroSys. ACM (2015)Google Scholar
  4. 4.
    Dupont, C., Sheikhalishahi, M., Facca, F.M., Hermenier, F.: An energy aware framework for virtual machine placement in cloud federated data centres. In: 8th IEEE/ACM International Conference on Utility and Cloud Computing, December 2015Google Scholar
  5. 5.
    Goiri, I.N., Katsak, W., Le, K., Nguyen, T.D., Bianchini, R.: Parasol and GreenSwitch: managing datacenters powered by renewable energy. In: SIGARCH Computer Architecture News, vol. 41, no. 1, March 2013Google Scholar
  6. 6.
    Goiri, I.N., Le, K., Haque, M.E., Beauchea, R., Nguyen, T.D., Guitart, J., Torres, J., Bianchini, R.: Greenslot: scheduling energy consumption in green datacenters. In: Proceedings of International Conference for High Performance Computing, Networking, Storage and Analysis. ACM (2011)Google Scholar
  7. 7.
    Goiri, I.N., Le, K., Nguyen, T.D., Guitart, J., Torres, J., Bianchini, R.: GreenHadoop: leveraging green energy in data-processing frameworks. In: Proceeding of the 7th ACM Eurosys. ACM (2012)Google Scholar
  8. 8.
    Hasan, M.S., de Oliveira, F.A., Ledoux, T., Pazat, J.L.: Enabling green energy awareness in interactive cloud application. In: 2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 414–422, December 2016Google Scholar
  9. 9.
    Li, Y., Orgerie, A.C., Menaud, J.M.: Opportunistic scheduling in clouds partially powered by green energy. In: IEEE International Conference on GreenCom (2015)Google Scholar
  10. 10.
    Liu, Z., Chen, Y., Bash, C., Wierman, A., Gmach, D., Wang, Z., Marwah, M., Hyser, C.: Renewable and cooling aware workload management for sustainable data centers. In: Proceeding of the 12th ACM SIGMETRICS. ACM (2012)Google Scholar
  11. 11.
    Menana, J., Demassey, S.: Sequencing and counting with the multicost-regular constraint. In: Hoeve, W.-J., Hooker, J.N. (eds.) CPAIOR 2009. LNCS, vol. 5547, pp. 178–192. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-01929-6_14 CrossRefGoogle Scholar
  12. 12.
    de Oliveira Jr., F.A., Ledoux, T.: Self-management of cloud applications and infrastructure for energy optimization. SIGOPS OSR 46(2), 10 (2012)CrossRefGoogle Scholar
  13. 13.
    Rossi, F., van Beek, P., Walsh, T. (eds.): Handbook of Constraint Programming, Foundations of Artificial Intelligence, vol. 2. Elsevier, Amsterdam (2006)Google Scholar
  14. 14.
    Sharma, N., Sharma, P., Irwin, D., Shenoy, P.: Predicting solar generation from weather forecasts using machine learning. In: IEEE International Conference on Smart Grid Communications, October 2011Google Scholar
  15. 15.
    The DC4Cities Consortium: D6.3 - Report on the experimentation phase 2. Evaluation report on the second trial cycle (2015). http://www.dc4cities.eu/
  16. 16.
    Wang, C., Nasiriani, N., Kesidis, G., Urgaonkar, B., Wang, Q., Chen, L.Y., Gupta, A., Birke, R.: Recouping energy costs from cloud tenants: tenant demand response aware pricing design. In: Proceedings of the 6th International Conference on Future Energy Systems. ACM (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Fabien Hermenier
    • 1
    • 2
  • Giuliani Giovanni
    • 3
  • Andre Milani
    • 3
  • Sophie Demassey
    • 4
  1. 1.Université Côte d’Azur, CNRS, I3SParisFrance
  2. 2.Nutanix Inc.San JoseUSA
  3. 3.Hewlett Packard EnterpriseMilanoItaly
  4. 4.Centre for Applied Mathematics – MINES ParisTech, PSLParisFrance

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