Skip to main content
Log in

Speed Scaling of Processes with Arbitrary Speedup Curves on a Multiprocessor

  • Published:
Theory of Computing Systems Aims and scope Submit manuscript

Abstract

We consider the setting of a multiprocessor where the speeds of the m processors can be individually scaled. Jobs arrive over time and have varying degrees of parallelizability. A nonclairvoyant scheduler must assign the processes to processors, and scale the speeds of the processors. We consider the objective of energy plus flow time. We assume that a processor running at speed s uses power s α for some constant α>1. For processes that may have side effects or that are not checkpointable, we show an \(\Omega(m^{(\alpha -1)/\alpha^{2}})\) bound on the competitive ratio of any randomized algorithm. For checkpointable processes without side effects, we give an O(log m)-competitive algorithm. Thus for processes that may have side effects or that are not checkpointable, the achievable competitive ratio grows quickly with the number of processors, but for checkpointable processes without side effects, the achievable competitive ratio grows slowly with the number of processors. We then show a lower bound of Ω(log 1/α m) on the competitive ratio of any randomized algorithm for checkpointable processes without side effects.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Albers, S., Fujiwara, H.: Energy-efficient algorithms for flow time minimization. ACM Trans. Algorithms 3(4), 49 (2007)

    Article  MathSciNet  Google Scholar 

  2. Bansal, N., Chan, H.-L., Lam, T.-W., Lee, L.-K.: Scheduling for bounded speed processors. In: International Colloquium on Automata, Languages and Programming, pp. 409–420 (2008)

    Chapter  Google Scholar 

  3. Bansal, N., Chan, H.-L., Pruhs, K.: Speed scaling with an arbitrary power function. In: ACM-SIAM Symposium on Discrete Algorithms, pp. 693–701 (2009)

    Google Scholar 

  4. Bansal, N., Pruhs, K., Stein, C.: Speed scaling for weighted flow time. In: ACM-SIAM Symposium on Discrete Algorithms, pp. 805–813 (2007)

    Google Scholar 

  5. Becchetti, L., Leonardi, S.: Nonclairvoyant scheduling to minimize the total flow time on single and parallel machines. J. ACM 51(4), 517–539 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  6. Becchetti, L., Leonardi, S., Marchetti-Spaccamela, A., Pruhs, K.: Online weighted flow time and deadline scheduling. J. Discrete Algorithms 4(3), 339–352 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  7. Chan, H.-L., Edmonds, J., Lam, T.-W., Lee, L.-K., Marcheti-Spaccamela, A., Pruhs, K.: Nonclairvoyant speed scaling for flow and energy. In: International Symposium on Theoretical Aspects of Computer Science, pp. 255–264 (2009)

    Google Scholar 

  8. Edmonds, J.: Scheduling in the dark. Theor. Comput. Sci. 235(1), 109–141 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  9. Edmonds, J., Pruhs, K.: Scalably scheduling processes with arbitrary speedup curves. In: ACM-SIAM Symposium on Discrete Algorithms, pp. 685–692 (2009)

    Google Scholar 

  10. Kalyanasundaram, B., Pruhs, K.: Speed is as powerful as clairvoyance. J. ACM 47(4), 617–643 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  11. Kalyanasundaram, B., Pruhs, K.: Minimizing flow time nonclairvoyantly. J. ACM 50(4), 551–567 (2003)

    Article  MathSciNet  Google Scholar 

  12. Lam, T.-W., Lee, L.-K., To, I., Wong, P.: Speed scaling functions for flow time scheduling based on active job count. In: European Symposium on Algorithms, pp. 647–659 (2008)

    Google Scholar 

  13. Merritt, R.: CPU designers debate multi-core future. EE Times, June 2008

  14. Motwani, R., Phillips, S., Torng, E.: Nonclairvoyant scheduling. Theor. Comput. Sci. 130(1), 17–47 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  15. Pruhs, K.: Competitive online scheduling for server systems. ACM SIGMETRICS Perform. Eval. Rev. 34(4), 52–58 (2007)

    Article  Google Scholar 

  16. Pruhs, K., Sgall, J., Torng, E.: Online scheduling. In: Handbook on Scheduling. CRC Press, Boca Raton (2004)

    Google Scholar 

  17. Pruhs, K., Uthaisombut, P., Woeginger, G.J.: Getting the best response for your erg. ACM Trans. Algorithms 4(3), 38 (2008)

    Article  MathSciNet  Google Scholar 

  18. Robert, J., Schabanel, N.: Non-clairvoyant batch sets scheduling: Fairness is fair enough. In: European Symposium on Algorithms, pp. 741–753 (2007)

    Google Scholar 

  19. Robert, J., Schabanel, N.: Non-clairvoyant scheduling with precedence constraints. In: ACM-SIAM Symposium on Discrete Algorithms, pp. 491–500 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kirk Pruhs.

Additional information

J. Edmonds was supported in part by NSERC Canada.

K. Pruhs was supported in part by an IBM faculty award, and by NSF grants CNS-0325353, CCF-0514058, IIS-0534531, and CCF-0830558.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chan, HL., Edmonds, J. & Pruhs, K. Speed Scaling of Processes with Arbitrary Speedup Curves on a Multiprocessor. Theory Comput Syst 49, 817–833 (2011). https://doi.org/10.1007/s00224-011-9349-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00224-011-9349-0

Keywords

Navigation