Extending Energetic Potential of Data Centers to Participate in Smart Grid Networks

Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 495)


Data centers are growing due to the increasing demand of new and growing IT services. Following these trends, the electrical power consumption in data centers becomes a significant value. In parallel, an increasing share of renewable and volatile power sources needs to be handled in power networks due to the energy transition in Germany. To compensate the volatile behavior of renewables, appropriate actions are needed.

To take advantage of these issues, we present our approach to adapt the data center’s power consumption. In our previous work, we pointed out the effects of applying different virtual machine allocation to data centers and to effect the server’s power consumption. According to this approach, a controllable amount of power can be a valuable contribution to smart grid networks to keep power networks stable. In this paper, we propose our approach basing on server virtualization technology to adapt the data center’s power consumption up to 50%. The approach is suitable in infrastructure as a service (IaaS) environments.


Smart grid Data center Server virtualization VM placement Energy efficiency Power-aware Resource management 


  1. 1.
    Aksanli, B., et al.: Utilizing green energy prediction to schedule mixed batch and service jobs in data centers. SIGOPS Oper. Syst. Rev. 45(3), 53–57 (2012)CrossRefGoogle Scholar
  2. 2.
    Barker, S., et al.: An empirical study of memory sharing in virtual machines. In: Proceedings of the 2012 USENIX conference on Annual Technical Conference, USENIX ATC 2012. USENIX Association, Berkeley, p. 25 (2012)Google Scholar
  3. 3.
    Beloglazov, A., Buyya, R.: OpenStack Neat: a framework for dynamic and energy-efficient consolidation of virtual machines in OpenStack clouds. In: Concurrency and Computation: Practice and Experience (2014)Google Scholar
  4. 4.
    Bobroff, N., et al.: Dynamic Placement of Virtual Machines for Managing SLA Violations. In: 2007 10th IFIP/IEEE International Symposium on Integrated Network Management, Munich, pp. 119–128 (2007)Google Scholar
  5. 5.
    Borderstep Institut für Innovation und Nachhaltigkeit gemeinnützige GmbH: Deutliches Wachstum bei deutschen Rechenzentren – Update 2015 (2015). Accessed 10 Jan 2017
  6. 6.
    Borgerding, A., Schomaker, G.: Extending energetic potentials of data centers by resource optimization to improve carbon footprint. In: Gómez, J.M., Sonnenschein, M., Vogel, U., Winter, A., Rapp, B., Giesen, N. (eds.) Advances and New Trends in Environmental and Energy Informatics. PI, pp. 3–19. Springer, Cham (2016). doi: 10.1007/978-3-319-23455-7_1 CrossRefGoogle Scholar
  7. 7.
    Casale G., et al.: A model of storage I/O performance interference in virtualized systems. In: 31st International Conference on Distributed Computing Systems Workshops, Minneapolis, pp. 34–39 (2011)Google Scholar
  8. 8.
    Chen, C., et al.: Green-aware workload scheduling in geographically distributed data centers. In: 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings, pp. 82–89 (2012)Google Scholar
  9. 9.
    Chen, H., et al: The data center as a grid load stabilizer. In: 2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC), Singapore, pp. 105–112 (2014)Google Scholar
  10. 10.
    CONSENTEC Consulting für Energiewirtschaft und -technik GmbH: Gutachten zur Dimensionierung des Regelleistungsbedarfs unter dem NRV (2010). Accessed 10 Jan 2017
  11. 11.
    Corradi, A., et al.: VM consolidation: a real case based on OpenStack Cloud. Fut. Gener. Comput. Syst. 32, 118–127 (2014)CrossRefGoogle Scholar
  12. 12.
    David, A., et al.: Model-based framework for schedulability analysis using UPPAAL 4.1. In: Model-Based Design for Embedded Systems, pp. 93–119 (2010)Google Scholar
  13. 13.
    Kumar, P., et al.: Power and data aware best fit algorithm for energy saving in cloud computing. Int. J. Comput. Sci. Inf. Technol. 5(5), 6712 (2014)Google Scholar
  14. 14.
    Krioukov, A., et al.: Integrating renewable energy using data analytics systems: challenges and opportunities. IEEE Data Eng. Bull. 34(1), 3–11 (2011)Google Scholar
  15. 15.
    Liu, H., et al.: Performance and energy modeling for live migration of virtual machines. In: Proceedings of the 20th International Symposium on High Performance Distributed Computing, HPDC 2011. ACM, New York, pp. 171–182 (2011)Google Scholar
  16. 16.
    Liu, Z., et al.: Greening geographical load balancing. In: ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2011, New York, pp. 233–244 (2011)Google Scholar
  17. 17.
    Pelley, S., et al.: Understanding and abstracting total data center power. In: Proceedings of the 2009 Workshop on Energy Efficient Design (WEED), June 2009Google Scholar
  18. 18.
    REGELLEISTUNG.NET: Internetplattform zur Vergabe von Regelleistung: Grid Control Cooperation (GCC). Accessed 10 Jan 2017
  19. 19.
    Standard Performance Evaluation Corporation (SPEC): Benchmark Results Summary of Fujitsu FUJITSU Server PRIMERGY TX2560 M1 (2015). Accessed 10 Jan 2017
  20. 20.
    Tang, Q., et al.: Thermal-aware task scheduling for data centers through minimizing heat recirculation. The IMPACT Laboratory School of Computing and Informatics Arizona State University Tempe, AZ 85287 (2008)Google Scholar
  21. 21.
    Wood, T., et al.: Memory buddies: exploiting page sharing for smart colocation in virtualized data centers. In: Proceedings of the 2009 ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments, VEE 2009, pp. 31–40. ACM, New York (2009)Google Scholar
  22. 22.
    Pu, X., et al.: Who is your neighbor: net i/o performance interference in virtualized clouds. IEEE Trans. Serv. Comput. 6, 314–329 (2013)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  1. 1.Carl Von Ossietzky Universität OldenburgOldenburgGermany
  2. 2.OFFIS-Institute for Information TechnologyOldenburgGermany

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