Interconnecting Smart Grids and Clouds to save Energy

Anne-Cécile Orgerie

2015

Abstract

Cloud computing is becoming an essential component for Internet services. However, its energy consumption has become a key environmental and economic concern. The distributed nature of Cloud infrastructures involves that their components are spread across wide areas, interconnected through different networks, and powered by diverse energy sources and providers, making overall energy monitoring and optimizations challenging. In this paper, we present the opportunity brought by the Smart Grids to exploit renewable energy availability and to optimize energy management in distributed Clouds. The presence of smart sensors which are both integrated into the electricity Grid and connected to the Internet, indeed offers for the first time the possibility of exploiting the availability of various energy sources, and of making complete energy measurements of all the Cloud resources – computing, storage and especially networking resources – problems which have previously been intractable.

References

  1. Baliga, J., Ayre, R., Hinton, K., and Tucker, R. (2011). Green cloud computing: Balancing energy in processing, storage, and transport. Proceedings of the IEEE, 99(1):149-167.
  2. Barham, P., Dragovic, B., Fraser, K., Hand, S., Harris, T., Ho, A., Neugebauer, R., Pratt, I., and Warfield, A. (2003). Xen and the Art of Virtualization. In ACM Symposium on Operating Systems Principles (SOSP), pages 164-177.
  3. Betti, G., Amaldi, E., Capone, A., and Ercolani, G. (2013). Cost-aware optimization models for communication networks with renewable energy sources. In IEEE Conference on Computer Communications Workshops (INFOCOM Workshops), pages 25-30.
  4. Bolla, R., Bruschi, R., Davoli, F., and Cucchietti, F. (2011). Energy Efficiency in the Future Internet: A Survey of Existing Approaches and Trends in Energy-Aware Fixed Network Infrastructures. IEEE Communications Surveys and Tutorials, 13(2):223-244.
  5. Chen, X. and Phillips, C. (2013). An evolutionary based dynamic energy management framework for ip-overdwdm core networks. In Pierson, J.-M., Da Costa, G., and Dittmann, L., editors, Energy Efficiency in Large Scale Distributed Systems, Lecture Notes in Computer Science, pages 233-247. Springer Berlin Heidelberg.
  6. Christensen, K., Reviriego, P., Nordman, B., Bennett, M., Mostowfi, M., and Maestro, J. (2010). IEEE 802.3az: the road to Energy Efficient Ethernet. IEEE Communications Magazine, 48(11):50-56.
  7. Elson, J. and Howell, J. (2008). Handling Flash Crowds from Your Garage. In USENIX Annual Technical Conference, ATC'08, pages 171-184.
  8. Fan, X., Weber, W.-D., and Barroso, L. A. (2007). Power provisioning for a warehouse-sized computer. In ACM International symposium on Computer architecture (ISCA), pages 13-23.
  9. Fang, X., Misra, S., Xue, G., and Yang, D. (2012). Smart Grid: The New and Improved Power Grid: A Survey. IEEE Communications Surveys Tutorials, 14(4):944- 980.
  10. Feng, X., Peterson, W., Yang, F., Wickramasekara, G., and Finney, J. (2009). Smarter grids are more efficient. ABB review.
  11. Figuerola, S., Lemay, M., Reijs, V., Savoie, M., and St. Arnaud, B. (2009). Converged Optical Network Infrastructures in Support of Future Internet and Grid Services Using IaaS to Reduce GHG Emissions. Journal of Lightwave Technology, 27(12):1941-1946.
  12. Goiri, I., Le, K., Haque, M., Beauchea, R., Nguyen, T., Guitart, J., Torres, J., and Bianchini, R. (2011). GreenSlot: Scheduling energy consumption in green datacenters. In International Conference for High Performance Computing, Networking, Storage and Analysis (SC), pages 1-11.
  13. Greenpeace (2011). How dirty is your data? Greenpeace report.
  14. Gunaratne, C., Christensen, K., and Suen, S. (2006). Ethernet Adaptive Link Rate (ALR): Analysis Of A Buffer Threshold Policy. In IEEE Global Telecommunications Conference (GLOBECOM), pages 1-6.
  15. Hledik, R. (2009). How green is the smart grid? The Electricity Journal, 22(3):29 - 41.
  16. Jain, R. and Paul, S. (2013). Network virtualization and software defined networking for cloud computing: a survey. IEEE Communications Magazine, 51(11):24- 31.
  17. Katz, R. H. (2009). Tech Titans Building Boom. IEEE Spectrum, 46(2):40-54.
  18. Koomey, J. (2011). Growth in Data Center Electricity Use 2005 to 2010. Analytics Press.
  19. Lara, A., Kolasani, A., and Ramamurthy, B. (2014). Network Innovation using OpenFlow: A Survey. IEEE Communications Surveys Tutorials, 16(1):493-512.
  20. Lefèvre, L. and Orgerie, A.-C. (2010). Designing and Evaluating an Energy Efficient Cloud. The Journal of SuperComputing, 51(3):352-373.
  21. Mills, M. (2013). The Cloud Begins With Coal - Big Data, Big Networks, Big Infrastructure, and Big Power. Technical report, Digital Power Group.
  22. Miyoshi, A., Lefurgy, C., Van Hensbergen, E., Rajamony, R., and Rajkumar, R. (2002). Critical power slope: understanding the runtime effects of frequency scaling. In ACM International conference on Supercomputing (ICS), pages 35-44.
  23. Orgerie, A.-C., de Assunc¸a˜o, M. D., and Lefèvre, L. (2014). A Survey on Techniques for Improving the Energy Efficiency of Large Scale Distributed Systems. ACM Computing Surveys, 46(4).
  24. Orgerie, A.-C., Lefèvre, L., and Guérin-Lassous, I. (2011). On the Energy Efficiency of Centralized and Decentralized Management for Reservation-Based Networks. In IEEE GLOBECOM.
  25. Pratt, R., Balducci, P., Gerkensmeyer, C., Katipamula, S., Kintner-Meyer, M., Sanquist, T., Schneider, K., and Secrest, T. (2010). The Smart Grid: An Estimation of the Energy and CO2 Benefits. Technical report, Pacific Northwest National Laboratory (PNNL), U.S. Department of Energy.
  26. Snowdon, D., Ruocco, S., and Heiser, G. (2005). Power Management and Dynamic Voltage Scaling: Myths and Facts. In Workshop on Power Aware Real-time Computing.
  27. Talaber, R., Brey, T., and Lamers, L. (2009). Using Virtualization to Improve Data Center Efficiency. Technical report, The Green Grid.
  28. Tang, S., Huang, Q., Li, X.-Y., and Wu, D. (2013). Smoothing the energy consumption: Peak demand reduction in smart grid. In IEEE Conference on Computer Communications (INFOCOM), pages 1133-1141.
  29. Wang, L., Tao, J., Kunze, M., Castellanos, A., Kramer, D., and Karl, W. (2008a). Scientific Cloud Computing: Early Definition and Experience. In IEEE International Conference on High Performance Computing and Communications (HPCC), pages 825-830.
  30. Wang, Y., Keller, E., Biskeborn, B., van der Merwe, J., and Rexford, J. (2008b). Virtual routers on the move: live router migration as a network-management primitive. ACM SIGCOMM Computer Communication Review, 38(4):231242.
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Paper Citation


in Harvard Style

Orgerie A. (2015). Interconnecting Smart Grids and Clouds to save Energy . In Proceedings of the 4th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-105-2, pages 376-381. DOI: 10.5220/0005484903760381


in Bibtex Style

@conference{smartgreens15,
author={Anne-Cécile Orgerie},
title={Interconnecting Smart Grids and Clouds to save Energy},
booktitle={Proceedings of the 4th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},
year={2015},
pages={376-381},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005484903760381},
isbn={978-989-758-105-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,
TI - Interconnecting Smart Grids and Clouds to save Energy
SN - 978-989-758-105-2
AU - Orgerie A.
PY - 2015
SP - 376
EP - 381
DO - 10.5220/0005484903760381