Advertisement

Optimal Speed Scaling with a Solar Cell

(Extended Abstract)
  • Neal Barcelo
  • Peter Kling
  • Michael Nugent
  • Kirk Pruhs
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10043)

Abstract

We consider the setting of a sensor that consists of a speed-scalable processor, a battery, and a solar cell that harvests energy from its environment at a time-invariant recharge rate. The processor must process a collection of jobs of various sizes. Jobs arrive at different times and have different deadlines. The objective is to minimize the recharge rate, which is the rate at which the device has to harvest energy in order to feasibly schedule all jobs. The main result is a polynomial-time combinatorial algorithm for processors with a natural set of discrete speed/power pairs.

References

  1. 1.
    Angelopoulos, S., Lucarelli, G., Nguyen, K.T.: Primal-dual and dual-fitting analysis of online scheduling algorithms for generalized flow time problems. In: Bansal, N., Finocchi, I. (eds.) ESA 2015. LNCS, vol. 9294, pp. 35–46. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  2. 2.
    Antoniadis, A., Barcelo, N., Consuegra, M.E., Kling, P., Nugent, M., Pruhs, K., Scquizzato, M.: Efficient computation of optimal energy and fractional weighted flow trade-off schedules. In: Symposium on Theoretical Aspects of Computer Science, pp. 63–74 (2014)Google Scholar
  3. 3.
    Bansal, N., Chan, H.L., Pruhs, K.: Speed scaling with a solar cell. Theor. Comput. Sci. 410(45), 4580–4587 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Bansal, N., Kimbrel, T., Pruhs, K.: Speed scaling to manage energy and temperature. J. ACM 54(1), 3:1–3:39 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Barcelo, N., Kling, P., Nugent, M., Pruhs, K.: Optimal speed scaling with a solar cell. arXiv:1609.02668 [cs.DS]
  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.
    Cole, D., Letsios, D., Nugent, M., Pruhs, K.: Optimal energy trade-off schedules. In: International Green Computing Conference, pp. 1–10 (2012)Google Scholar
  8. 8.
    Pruhs, K., Uthaisombut, P., Woeginger, G.J.: Getting the best response for your ERG. ACM Trans. Algorithms 4(3), 38:1–38:17 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Remick, K., Quinn, D.D., McFarland, D.M., Bergman, L., Vakakis, A.: High-frequency vibration energy harvesting from impulsive excitation utilizing intentional dynamic instability caused by strong nonlinearity. J. Sound Vib. 370, 259–279 (2016)CrossRefGoogle Scholar
  10. 10.
    Stephen, N.G.: On energy harvesting from ambient vibration. J. Sound Vib. 293(1–2), 409–425 (2006)CrossRefGoogle Scholar
  11. 11.
    Vullers, R., van Schaijk, R., Doms, I., Hoof, C.V., Mertens, R.: Micropower energy harvesting. Solid-State Electron. 53(7), 684–693 (2009)CrossRefGoogle Scholar
  12. 12.
    Yao, F.F., Demers, A.J., Shenker, S.: A scheduling model for reduced CPU energy. In: Foundations of Computer Science, pp. 374–382 (1995)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Neal Barcelo
    • 1
  • Peter Kling
    • 2
  • Michael Nugent
    • 1
  • Kirk Pruhs
    • 1
  1. 1.Department of Computer ScienceUniversity of PittsburghPittsburghUSA
  2. 2.Universität HamburgHamburgGermany

Personalised recommendations