Skip to main content
Log in

Online Speed Scaling Based on Active Job Count to Minimize Flow Plus Energy

  • Published:
Algorithmica Aims and scope Submit manuscript

Abstract

This paper is concerned with online scheduling algorithms that aim at minimizing the total flow time plus energy usage. The results are divided into two parts. First, we consider the well-studied “simple” speed scaling model and show how to analyze a speed scaling algorithm (called AJC) that changes speed discretely. This is in contrast to the previous algorithms which change the speed continuously. More interestingly, AJC admits a better competitive ratio, and without using extra speed. In the second part, we extend the study to a more general speed scaling model where the processor can enter a sleep state to further save energy. A new sleep management algorithm called IdleLonger is presented. This algorithm, when coupled with AJC, gives the first competitive algorithm for minimizing total flow time plus energy in the general model.

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.

Fig. 1
Algorithm 1
Algorithm 2

Similar content being viewed by others

Notes

  1. Static power is dissipated due to leakage current and is independent of processor speed, and dynamic power is due to dynamic switching loss and increases with the speed.

  2. Young’s Inequality is stated as follows. Let f be a real-valued, continuous and strictly increasing function such that f(0)=0. Then, for all g,h≥0, \(\int _{0}^{g}\!f(x)\,\textup {d}x + \int _{0}^{h}\!f^{-1}(x)\,\textup {d}x \ge gh\), where f −1 is the inverse function of f. When using Young’s inequality to adapt the bound of \(\frac {\textup {d}\Phi_{2}}{\textup {d}t}\), we set f(x)=x α−1, f −1(x)=x 1/(α−1), g=s o/μ, and h=(n a+σ)1−1/α μ.

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. Augustine, J., Irani, S., Swamy, C.: Optimal power-down strategies. In: Proceedings of IEEE Symposium on Foundations of Computer Science (FOCS), pp. 530–539 (2004)

    Chapter  Google Scholar 

  3. Baker, K.R.: Introduction to Sequencing and Scheduling. Wiley, New York (1974)

    Google Scholar 

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

    Google Scholar 

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

    Chapter  Google Scholar 

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

    Google Scholar 

  7. Benini, L., Bogliolo, A., de Micheli, G.: A survey of design techniques for system-level dynamic power management. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 8(3), 299–316 (2000)

    Article  Google Scholar 

  8. Brooks, D.M., Bose, P., Schuster, S.E., Jacobson, H., Kudva, P.N., Buyuktosunoglu, A., Wellman, J.D., Zyuban, V., Gupta, M., Cook, P.W.: Power-aware microarchitecture: design and modeling challenges for next-generation microprocessors. IEEE MICRO 20(6), 26–44 (2000)

    Article  Google Scholar 

  9. Chan, H.L., Chan, W.T., Lam, T.W., Lee, L.K., Mak, K.S., Wong, P.W.H.: Energy efficient online deadline scheduling. In: Proceedings of ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 795–804 (2007)

    Google Scholar 

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

    Google Scholar 

  11. Gandhi, A., Gupta, V., Harchol-Balter, M., Kozuch, M.A.: Optimality analysis of energy-performance trade-off for server farm management. Perform. Eval. 67(11), 1155–1171 (2010)

    Article  Google Scholar 

  12. Greiner, G., Nonner, T., Souza, A.: The bell is ringing in speed-scaled multiprocessor scheduling. In: Proceedings of ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), pp. 11–18 (2009)

    Google Scholar 

  13. Hardy, G.H., Littlewood, J.E., Polya, G.: Inequalities. Cambridge University Press, Cambridge (1952)

    MATH  Google Scholar 

  14. Irani, S., Pruhs, K.: Algorithmic problems in power management. ACM SIGACT News 32(2), 63–76 (2005)

    Article  Google Scholar 

  15. Irani, S., Shukla, S., Gupta, R.: Online strategies for dynamic power management in systems with multiple power-saving states. ACM Trans. Embed. Comput. Syst. 2(3), 325–346 (2003)

    Article  Google Scholar 

  16. Irani, S., Shukla, S., Gupta, R.K.: Algorithms for power savings. ACM Trans. Algorithms 3(4), 41 (2007)

    Article  MathSciNet  Google Scholar 

  17. Karlin, A., Manasse, M., McGeoch, L., Owicki, S.: Competitive randomized algorithms for non-uniform problems. In: Proceedings of ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 301–309 (1990)

    Google Scholar 

  18. Lam, T.W., Lee, L.K., To, I.K.K., Wong, P.W.H.: Competitive non-migratory scheduling for flow time and energy. In: Proceedings of ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), pp. 256–264 (2008)

    Google Scholar 

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

    Google Scholar 

  20. Mudge, T.: Power: a first-class architectural design constraint. Computer 34(4), 52–58 (2001)

    Article  Google Scholar 

  21. Schrage, L.: A proof of the optimality of the shortest remaining processing time discipline. Oper. Res. 16(3), 687–690 (1968)

    Article  MathSciNet  MATH  Google Scholar 

  22. Yao, F., Demers, A., Shenker, S.: A scheduling model for reduced CPU energy. In: Proceedings of IEEE Symposium on Foundations of Computer Science (FOCS), pp. 374–382 (1995)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Prudence W. H. Wong.

Additional information

T.W. Lam is partially supported by HKU Grant 201007176149.

I.K.K. To and P.W.H. Wong are partially supported by EPSRC Grant EP/E028276/1.

The results in this paper have appeared in preliminary form in the proceedings of the 16th Annual European Symposium on Algorithms, 2008 and the proceedings of the 36th International Colloquium on Automata, Languages and Programming, 2009.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lam, TW., Lee, LK., To, I.K.K. et al. Online Speed Scaling Based on Active Job Count to Minimize Flow Plus Energy. Algorithmica 65, 605–633 (2013). https://doi.org/10.1007/s00453-012-9613-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00453-012-9613-y

Keywords

Navigation