Speed Scaling to Manage Temperature

  • Leon Atkins
  • Guillaume Aupy
  • Daniel Cole
  • Kirk Pruhs
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6595)

Abstract

We consider the speed scaling problem where the quality of service objective is deadline feasibility and the power objective is temperature. In the case of batched jobs, we give a simple algorithm to compute the optimal schedule. For general instances, we give a new online algorithm, and obtain an upper bound on the competitive ratio of this algorithm that is an order of magnitude better than the best previously known bound upper bound on the competitive ratio for this problem.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Albers, S.: Algorithms for energy saving. In: Albers, S., Alt, H., Näher, S. (eds.) Efficient Algorithms. LNCS, vol. 5760, pp. 173–186. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Albers, S.: Energy-efficient algorithms. Commun. ACM 53(5), 86–96 (2010)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Bansal, N., Bunde, D.P., Chan, H.L., Pruhs, K.: Average rate speed scaling. In: Laber, E.S., Bornstein, C., Nogueira, L.T., Faria, L. (eds.) LATIN 2008. LNCS, vol. 4957, pp. 240–251. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Bansal, N., Chan, H.L., Pruhs, K., Katz, D.: Improved bounds for speed scaling in devices obeying the cube-root rule. In: Albers, S., Marchetti-Spaccamela, A., Matias, Y., Nikoletseas, S., Thomas, W. (eds.) ICALP 2009. LNCS, vol. 5555, pp. 144–155. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Bansal, N., Kimbrel, T., Pruhs, K.: Speed scaling to manage energy and temperature. J. ACM 54(1), 1–39 (2007)CrossRefMATHMathSciNetGoogle Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    Chrobak, M., Dürr, C., Hurand, M., Robert, J.: Algorithms for temperature-aware task scheduling in microprocessor systems. In: Fleischer, R., Xu, J. (eds.) AAIM 2008. LNCS, vol. 5034, pp. 120–130. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Irani, S., Pruhs, K.R.: Algorithmic problems in power management. SIGACT News 36(2), 63–76 (2005)CrossRefGoogle Scholar
  9. 9.
    Li, M., Yao, A.C., Yao, F.F.: Discrete and continuous min-energy schedules for variable voltage processors. Proceedings of the National Academy of Sciences of the United States of America 103(11), 3983–3987 (2006)CrossRefGoogle Scholar
  10. 10.
    Rao, R., Vrudhula, S.: Performance optimal processor throttling under thermal constraints. In: Proceedings of the 2007 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems, CASES 2007, pp. 257–266. ACM, New York (2007)CrossRefGoogle Scholar
  11. 11.
    Snowdon, D.C., Ruocco, S., Heiser, G.: Power management and dynamic voltage scaling: Myths and facts. In: Proceedings of the 2005 Workshop on Power Aware Real-time Computing, New Jersey, USA (September 2005)Google Scholar
  12. 12.
    Yao, F., Demers, A., Shenker, S.: A scheduling model for reduced cpu energy. In: FOCS 1995: Proceedings of the 36th Annual Symposium on Foundations of Computer Science, p. 374. IEEE Computer Society Press, Washington, DC (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Leon Atkins
    • 1
  • Guillaume Aupy
    • 2
  • Daniel Cole
    • 3
  • Kirk Pruhs
    • 4
  1. 1.Department of Computer ScienceUniversity of BristolUK
  2. 2.Computer Science DepartmentENS LyonFrance
  3. 3.Computer Science DepartmentUniversity of PittsburghUSA
  4. 4.Computer Science DepartmentUniversity of PittsburghUSA

Personalised recommendations