Skip to main content

Quantitative Comparison of Power Management Algorithms

  • Chapter
  • 1070 Accesses

Abstract

Dynamic power management saves power by shutting down idle devices. Several management algorithms have been proposed and demonstrated effective in certain applications. We quantitatively compare the power saving and performance impact of these algorithms on hard disks of a desktop and a notebook computers. This paper has three contributions. First, we build a framework in Windows NT to implement power managers running realistic workloads and directly interacting with users. Second, we define performance degradation that reflects user perception. Finally, we compare power saving and performance of existing algorithms and analyze the difference.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. ACPI. http://www.teleport.com/ ¯acpi.

  2. L. Benini, A. Bogliolo, and G. D. Micheli. A Survey of Design Techniques for System-Level Dynamic Power Management. IEEE Transactions on VLSI Systems, March 2000.

    Google Scholar 

  3. E.-Y. Chung, L. Benini, A. Bogliolo, and G. D. Micheli. Dynamic Power Management for Non-Stationary Service Requests. In Design Automation and Test in Europe, pp. 77–81, 1999.

    Google Scholar 

  4. E.-Y. Chung, L. Benini, and G. D. Micheli. Dynamic power management using adaptive learning tree. In International Conference on Computer-Aided Design, pp. 274–279, 1999.

    Google Scholar 

  5. F. Douglis, R. Cáceres, F. Kaashoek, K. Li, B. Marsh, and J. A. Tauber. Storage Alternatives for Mobile Computers. In USENIX Symposium on Operating Systems Design and Implementation, pp. 25–37, 1994.

    Google Scholar 

  6. F. Douglis, P. Krishnan, and B. Bershad. Adaptive Disk Spin-Down Policies for Mobile Computers. In Computing Systems, 8, 381–413, 1995.

    Google Scholar 

  7. F. Douglis, P. Krishnan, and B. Marsh. Thwarting the Power-Hungry Disk. In USENIX Winter Conference, pp. 293–306, 1994.

    Google Scholar 

  8. R. Golding, P. Bosch, and J. Wilkes. Idleness is not Sloth. In USENIX Winter Conference, pp. 201–212, 1995.

    Google Scholar 

  9. C.-H. Hwang and A. C. Wu. A Predictive System Shutdown Method for Energy Saving of Event-Driven Computation. In International Conference on Computer-Aided Design, pp. 28–32, 1997.

    Google Scholar 

  10. Intel. Mobile Power Guide ‘99.

    Google Scholar 

  11. A. Karlin, M. Manasse, L. McGeoch, and S. Owicki. Competitive Randomized Algorithms for Nonuniform Problems. Algorithmica, 11(6):542–571, June 1994.

    Article  MATH  MathSciNet  Google Scholar 

  12. J. R. Lorch and A. J. Smith. Software Strategies for Portable Computer Energy Management. IEEE Personal Communications, 5(3):60–73, June 1998.

    Article  Google Scholar 

  13. Y.-H. Lu and G. D. Micheli. Adaptive Hard Disk Power Management on Personal Computers. In Great Lakes Symposium on VLSI, pp. 50–53, 1999.

    Google Scholar 

  14. Y.-H. Lu, T. Šimunić, and G. D. Micheli. Software Controlled Power Management. In International Workshop on Hardware/Software Codesign, pp. 157–161, 1999.

    Google Scholar 

  15. G. A. Paleologo, L. Benini, A. Bogliolo, and G. D. Micheli. Policy Optimization for Dynamic Power Management. In Design Automation Conference, pp. 182–187, 1998.

    Google Scholar 

  16. Q. Qiu and M. Pedram. Dynamic Power Management Based on Continuous-Time Markov Decision Processes. In Design Automation Conference, pp. 555–561, 1999.

    Google Scholar 

  17. M. B. Srivastava, A. P. Chandrakasan, and R. W. Brodersen. Predictive System Shutdown and Other Architecture Techniques for Energy Efficient Programmable Computation. IEEE Transactions on VLSI Systems, 4(1):42–55, March 1996.

    Article  Google Scholar 

  18. M. T. Stokes. Time in Human-Computer Interaction. PhD. Thesis, Psychology Department, Texas Tech University, 1991.

    Google Scholar 

  19. T. Šimunić, L. Benini, and G. D. Micheli. Event-Driven Power Management of Portable Systems. In International Symposium on System Synthesis, pp. 18–23, 1999.

    Google Scholar 

  20. T. Šimunić, L. Benini, and G. D. Micheli. Dynamic Power Management of Laptop Hard Disk. In Design Automation and Test in Europe, 2000.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer

About this chapter

Cite this chapter

Lu, YH., Chung, EY., Šimunić, T., Benini, L., De Micheli, G. (2008). Quantitative Comparison of Power Management Algorithms. In: Lauwereins, R., Madsen, J. (eds) Design, Automation, and Test in Europe. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6488-3_16

Download citation

  • DOI: https://doi.org/10.1007/978-1-4020-6488-3_16

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-1-4020-6487-6

  • Online ISBN: 978-1-4020-6488-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics