IT-Cooling Collaborative Control Methods for Battery-Aware IT-Systems Targeting India

  • Tadayuki Matsumura
  • Tetsuya Yamada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7453)

Abstract

Two IT-system control methods, which realize efficient battery usage for battery-powered IT systems targeting developing countries such as India, are proposed. The proposed methods control the IT equipment and cooling power collaboratively on the basis of a forecast of power outage duration. To quantitatively evaluate these methods, power outages in Bangalore, India, were measured. The proposed methods were evaluated by using this measured power outage data. According to the evaluation results, the proposed methods can improve a measure of battery efficiency, namely, IT-used-energy (Q t )/battery-used-energy (Q u ), by 39% compared to that of conventional IT systems.

Keywords

Forecast Error Constant Power Rate Capacity Battery Capacity Cool Power 
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.
    Pedram, M., Wu, Q.: Design Considerations for Battery-Powered Electronics. In: Proc. Design Automation Conf., pp. 861–866 (June 1999)Google Scholar
  2. 2.
    Lahiri, K., Raghunathan, A., Dey, S.: Battery-Driven System Design: A New Frontier in Low Power Design. In: Proc. Int’l Conf. VLSI Design/7th Asia and South Pacific Design Automation Conf., pp. 261–267 (2002)Google Scholar
  3. 3.
    Luo, J., Jha, N.K.: Battery-Aware Static Scheduling for Distributed Real-Time Embedded Systems. In: Proc. Design Automation Conf., pp. 444–449 (2001)Google Scholar
  4. 4.
    Chowdhury, P., Chakrabarti, C.: Static Task-scheduling Algorithms for Battery-powered DVS Systems. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 13, 226 (2005)CrossRefGoogle Scholar
  5. 5.
    Ravi, N., Scott, J., Lu, H., Iftode, L.: Context-aware Battery Management for Mobile Phones. In: Proc. Conf. on Pervasive Computing and Communications, pp. 224–233 (2008)Google Scholar
  6. 6.
    Gmach, D., Rolia, J., Cherkasova, L., Kemper, A.: Workload Analysis and Demand Prediction of Enterprise Data Center Applications. In: Proc. of 10th Int’l Symp. on Workload Characterization, pp. 171–180 (September 2007)Google Scholar
  7. 7.
    Nicolescu, V., Gmach, D., Mohr, M., Kemper, A., Krcmar, H.: Evaluation of Adaptive Computing Concepts for Classical ERP Systems and Enterprise Services. In: Proc. CEC and EEE 2006, p. 48 (2006)Google Scholar
  8. 8.
    Gou, B., Wu, W.: Is the Prediction of Power System Blackouts Possible? In: Proc. Power and Energy Society General Meeting, pp. 1–4 (2008)Google Scholar
  9. 9.
    Panasonic Storage Battery Co., Ltd., LC-V1233 datasheetGoogle Scholar
  10. 10.
    Gold, S.: A PSPICE Macromodel for Lithium-Ion Batteries. In: The Battery Conference, pp. 215–222 (1997)Google Scholar
  11. 11.
    Hamilton, J.D.: Time Series Analysis. Princeton University Press, Princeton (1994)MATHGoogle Scholar
  12. 12.
    Taylor, J.W.: Short-term Electricity Demand Forecasting Using Double Seasonal Exponential Smoothing. J. Oper. Res. Soc. 54, 799–805 (2003)MATHCrossRefGoogle Scholar
  13. 13.
    Hyndman, R., Khandakar, Y.: Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software 27(3) (2008)Google Scholar
  14. 14.
    Standard Performance Evaluation Corporation, http://www.spec.org/power_ssj2008/

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tadayuki Matsumura
    • 1
  • Tetsuya Yamada
    • 1
  1. 1.Central Research LaboratoryHitachi Ltd.Kokubunji-shiJapan

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