Modeling and Simulation of Data Center Energy-Efficiency in CoolEmAll

  • Micha vor dem Berge
  • Georges Da Costa
  • Andreas Kopecki
  • Ariel Oleksiak
  • Jean-Marc Pierson
  • Tomasz Piontek
  • Eugen Volk
  • Stefan Wesner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7396)


In this paper we present an overview of the CoolEmAll project which addresses the important problem of data center energy efficiency. To this end, CoolEmAll aims at delivering advanced simulation, visualization and decision support tools along with open models of data center building blocks to be used in simulations. Both building blocks and the toolkit will take into account aspects that have major impact on actual energy consumption such as cooling solutions, properties of applications, and workload and resource management policies. In the paper we describe the CoolEmAll approach, its expected results and an environment for their verification.


data centers energy efficiency simulations 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bak, S., Krystek, M., Kurowski, K., Oleksiak, A., Piatek, W., Weglarz, J.: GSSIM - a Tool for Distributed Computing Experiments. Scientific Programming 19(4), 231–251 (2011)Google Scholar
  2. 2.
    Hintemann, R., Fichter, K.: Materialbestand der Rechenzentren in Deutschland, Eine Bestandsaufnahme zur Ermittlung von Ressourcen- und Energieeinsatz, UBA, Texte, 55/2010 (2010)Google Scholar
  3. 3.
    Christmann: Description for Resource Efficient Computing System (RECS) (2009),
  4. 4.
    Da Costa, G., Pierson, J.-M.: Characterizing Applications from Power Consumption: A Case Study for HPC Benchmarks. In: Kranzlmüller, D., Toja, A.M. (eds.) ICT-GLOW 2011. LNCS, vol. 6868, pp. 10–17. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Da Costa, G., Hlavacs, H., Hummel, K., Pierson, J.-M.: Modeling the Energy Consumption of Distributed Applications. In: Handbook of Energy-Aware and Green Computing. Chapman & Hall, CRC Press (2012)Google Scholar
  6. 6.
    Kipp, A., Schubert, L., Liu, J., Jiang, T., Christmann, W., vor dem Berge, M.: Energy Consumption Optimisation in HPC Service Centres. In: Topping, B.H.V., Ivanyi, P. (eds.) Proceedings of the Second International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering. Civil-Comp Press, Stirlingshire (2011)Google Scholar
  7. 7.
    Kipp, A., Jiang, T., Fugini, M., et al.: Layered Green Performance Indicators. Future Generation Computer Systems 28(2), 478–489 (2012)CrossRefGoogle Scholar
  8. 8.
    Koomey, J.: Worldwide electricity used in data centers. Environmental Research Letters 3(034008) (September 23, 2008)Google Scholar
  9. 9.
    Krystek, M., Kurowski, K., Oleksiak, A., Piatek, W.: Energy-aware simulations with GSSIM. In: 1st Year Proceedings of the COST Action IC0804 (2010)Google Scholar
  10. 10.
    Kurowski, K., Oleksiak, A., Piatek, W., Weglarz, J.: Hierarchical Scheduling Strategies for Parallel Tasks and Advance Reservations in Grids. Journal of Scheduling 11(14), 1–20 (2011), doi:10.1007/s10951-011-0254-9Google Scholar
  11. 11.
    Pierson, J.-M.: Energy: A New Criteria for Performances in Large Scale Distributed Systems. In: Hummel, K.A., Hlavacs, H., Gansterer, W. (eds.) PERFORM 2010. LNCS, vol. 6821, pp. 38–48. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Wierse, A., Lang, U., Rühle, R.: A System Architecture for Data-Oriented Visualization. In: Lee, J.P., Grinstein, G.G. (eds.) Visualization-WS 1993. LNCS, vol. 871, pp. 148–159. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  13. 13.
    Weller, H.G., Tabor, G., Jasak, H., Fureby, C.: A tensorial approach to computational continuum mechanics using object-oriented techniques. Computers in Physics 12(6), 620–631 (1998)CrossRefGoogle Scholar
  14. 14.
    Volk, E., Buchholz, J., Wesner, S., Koudela, D., Schmidt, M., et al.: Towards Intelligent Management of Very Large Computing Systems. In: Proceedings of the Competence in High Performance Computing 2010 (2012)Google Scholar
  15. 15.
    Berl, A., Gelenbe, E., di Girolamo, M., Giuliani, G., de Meer, H., Dang, M.-Q., Pentikousis, K.: Energy-Efficient Cloud Computing. The Computer Journal 53(7) (2010)Google Scholar
  16. 16.
    Gelenbe, E., Morfopoulou, C.: Power savings in packet networks via optimised routing. Mobile Networks and Applications 17(1), 152–159 (2012)CrossRefGoogle Scholar
  17. 17.
    Klingert, S., Schulze, T., Bunse, C.: GreenSLAs for the Energy-efficient Management of Data Centres. In: 2nd International Conference on Energy-Efficient Computing and Networking, e-Energy (2011)Google Scholar
  18. 18.
    CoolEmAll Report D3.1: First definition of the flexible rack-level ComputeBox with integrated cooling,
  19. 19.
  20. 20.
    The CoolEmAll project website,
  21. 21.
  22. 22.
    The MontBlanc project website,
  23. 23.
  24. 24.
  25. 25.
    TIMaCS (Tools for Intelligent Management of very large Computing Systems) project website,

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Micha vor dem Berge
    • 1
  • Georges Da Costa
    • 4
  • Andreas Kopecki
    • 2
  • Ariel Oleksiak
    • 3
  • Jean-Marc Pierson
    • 4
  • Tomasz Piontek
    • 3
  • Eugen Volk
    • 2
  • Stefan Wesner
    • 2
  1. 1.Christmann Informationstechnik + MedienGermany
  2. 2.High Performance Computing Center StuttgartGermany
  3. 3.Poznan Supercomputing and Networking CenterPoland
  4. 4.IRITUniversity of ToulouseFrance

Personalised recommendations