Building a Microscope for the Data Center

  • Nuno Pereira
  • Stefano Tennina
  • Eduardo Tovar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7405)

Abstract

Managing the physical and compute infrastructure of a large data center is an embodiment of a Cyber-Physical System (CPS). The physical parameters of the data center (such as power, temperature, pressure, humidity) are tightly coupled with computations, even more so in upcoming data centers, where the location of workloads can vary substantially due, for example, to workloads being moved in a cloud infrastructure hosted in the data center. In this paper, we describe a data collection and distribution architecture that enables gathering physical parameters of a large data center at a very high temporal and spatial resolution of the sensor measurements. We think this is an important characteristic to enable more accurate heat-flow models of the data center and with them, find opportunities to optimize energy consumption. Having a high resolution picture of the data center conditions, also enables minimizing local hotspots, perform more accurate predictive maintenance (pending failures in cooling and other infrastructure equipment can be more promptly detected) and more accurate billing. We detail this architecture and define the structure of the underlying messaging system that is used to collect and distribute the data. Finally, we show the results of a preliminary study of a typical data center radio environment.

Keywords

Sensor Network Sensor Node Wireless Sensor Network Data Center Cluster Head 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Lee, E.K., Kulkarni, I.S., Pompili, D., Parashar, M.: Proactive thermal management in green datacenters. Journal of Supercomputing 51(1), 1–31 (2010)CrossRefGoogle Scholar
  2. 2.
    Heath, T., Centeno, A.P., George, P., Ramos, L., Jaluria, Y., Bianchini, R.: Mercury and freon: temperature emulation and management for server systems. SIGOPS Oper. Syst. Rev. 40(5), 106–116 (2006)CrossRefGoogle Scholar
  3. 3.
    Bohrer, P., Elnozahy, E.N., Keller, T., Kistler, M., Lefurgy, C., McDowell, C., Rajamony, R.: The case for power management in web servers. In: Power Aware Computing, pp. 261–289. Kluwer Academic Publishers, Norwell (2002)Google Scholar
  4. 4.
    Xu, R., Zhu, D., Rusu, C., Melhem, R., Mossé, D.: Energy-efficient policies for embedded clusters. In: Proceedings of the 2005 ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems, LCTES 2005, pp. 1–10. ACM, New York (2005)CrossRefGoogle Scholar
  5. 5.
    Horvath, T., Abdelzaher, T., Skadron, K., Liu, X.: Dynamic voltage scaling in multitier web servers with end-to-end delay control. IEEE Trans. Comput. 56(4), 444–458 (2007)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Meisner, D., Gold, B.T., Wenisch, T.F.: Powernap: eliminating server idle power. In: Proceedings of the 14th International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2009, pp. 205–216. ACM, New York (2009)CrossRefGoogle Scholar
  7. 7.
    Wang, S., Chen, J.-J., Liu, J., Liu, X.: Power saving design for servers under response time constraint. In: Proceedings of the 2010 22nd Euromicro Conference on Real-Time Systems, ECRTS 2010, pp. 123–132. IEEE Computer Society, Washington, DC (2010)Google Scholar
  8. 8.
    Parolini, L., Sinopoli, B., Krogh, B.H.: Reducing data center energy consumption via coordinated cooling and load management. In: Proceedings of the 2008 Conference on Power Aware Computing and Systems, HotPower 2008, p. 14. USENIX Association, Berkeley (2008)Google Scholar
  9. 9.
    Zhou, R., Wang, Z., Bash, C.E., McReynolds, A.: Data center cooling management and analysis – a model based approach. In: 28th Annual Semiconductor Thermal Measurement, Modeling and Management Symposium (SEMI-THERM 2012), San Jose, California, USA (March 2012)Google Scholar
  10. 10.
    Rambo, J., Joshi, Y.: Modeling of data center airflow and heat transfer: State of the art and future trends. Distrib. Parallel Databases 21(2-3), 193–225 (2007)CrossRefGoogle Scholar
  11. 11.
    Liang, C.-J.M., Liu, J., Luo, L., Terzis, A., Zhao, F.: Racnet: a high-fidelity data center sensing network. In: Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, SenSys 2009, pp. 15–28. ACM, New York (2009)CrossRefGoogle Scholar
  12. 12.
    Weiss, B., Truong, H.L., Schott, W., Scherer, T., Lombriser, C., Chevillat, P.: Wireless sensor network for continuously monitoring temperatures in data centers. In: IBM RZ 3807 (2011)Google Scholar
  13. 13.
    Viswanathan, H., Lee, E.K., Pompili, D.: Self-organizing sensing infrastructure for autonomic management of green datacenters. IEEE Network 25(4), 34–40 (2011)CrossRefGoogle Scholar
  14. 14.
    Schmidt, R.R., Cruz, E.E., Iyengar, M.: Challenges of data center thermal management. IBM Journal of Research and Development 49(4.5), 709–723 (2005)CrossRefGoogle Scholar
  15. 15.
    Fredrik Karlsson, J., Moshfegh, B.: Investigation of indoor climate and power usage in a data center. Energy and Buildings 37(10), 1075–1083 (2005)CrossRefGoogle Scholar
  16. 16.
    Rowe, A., Berge, M.E., Rajkumar, R.: Sensor andrew: Large-scale campus-wide sensing and actuation. International Business 55(1), 1–14 (2011)Google Scholar
  17. 17.
    Locke, D.: Mq telemetry transport (mqtt) v3.1 protocol specification (2010)Google Scholar
  18. 18.
    Stanford-Clark, A.J., Wightwick, G.R.: The application of publish/subscribe messaging to environmental, monitoring, and control systems. IBM J. Res. Dev. 54(4), 396–402 (2010)CrossRefGoogle Scholar
  19. 19.
    Wilson, S., Frey, J.: The smartlab: Experimental and environmental control and monitoring of the chemistry laboratory. In: Proceedings of the 2009 International Symposium on Collaborative Technologies and Systems, CTS 2009, pp. 85–90. IEEE Computer Society, Washington, DC (2009)CrossRefGoogle Scholar
  20. 20.
    Ganev, V., Chodos, D., Nikolaidis, I., Stroulia, E.: The smart condo: integrating sensor networks and virtual worlds. In: Proceedings of the 2nd Workshop on Software Engineering for Sensor Network Applications, SESENA 2011, pp. 49–54. ACM, New York (2011)CrossRefGoogle Scholar
  21. 21.
    Aberer, K., Hauswirth, M., Salehi, A.: A middleware for fast and flexible sensor network deployment. In: Proceedings of the 32nd international conference on Very large data bases, VLDB 2006, pp. 1199–1202. VLDB Endowment (2006)Google Scholar
  22. 22.
    About us - Pachube (2007), http://community.pachube.com/about
  23. 23.
    Modbus over serial line - specification & implementation guide - v1.0 (February 2002), http://www.modbus.org/docs/Modbus_over_serial_line_V1.pdf
  24. 24.
    Xmpp standards foundation, http://xmpp.org
  25. 25.
  26. 26.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Nuno Pereira
    • 1
  • Stefano Tennina
    • 1
  • Eduardo Tovar
    • 1
  1. 1.CISTER/INESC-TEC, ISEP, Polytechnic Institute of PortoPortoPortugal

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