Mechanism Design for Aggregating Energy Consumption and Quality of Service in Speed Scaling Scheduling

  • Christoph Dürr
  • Łukasz Jeż
  • Óscar C. Vásquez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8289)


We consider a strategic game, where players submit jobs to a machine that executes all jobs in a way that minimizes energy while respecting the jobs’ deadlines. The energy consumption is then charged to the players in some way. Each player wants to minimize the sum of that charge and of their job’s deadline multiplied by a priority weight. Two charging schemes are studied, the proportional cost share which does not always admit pure Nash equilibria, and the marginal cost share, which does always admit pure Nash equilibria, at the price of overcharging by a constant factor.


Mechanism Design Cost Share Strategic Game Potential Game Pure Nash Equilibrium 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Albers, S.: Energy-efficient algorithms. Communications of the ACM 53(5), 86–96 (2010)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Albers, S., Fujiwara, H.: Energy-efficient algorithms for flow time minimization. ACM Transactions on Algorithms (TALG) 3(4), 49 (2007)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Angel, E., Bampis, E., Kacem, F.: Energy aware scheduling for unrelated parallel machines. In: 2012 IEEE International Conference on Green Computing and Communications (GreenCom), pp. 533–540. IEEE (2012)Google Scholar
  4. 4.
    Bansal, N., Chan, H.-L., Katz, D., Pruhs, K.: Improved bounds for speed scaling in devices obeying the cube-root rule. Theory of Computing 8, 209–229 (2012)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Bansal, N., Kimbrel, T., Pruhs, K.: Speed scaling to manage energy and temperature. Journal of the ACM (JACM) 54(1), 3 (2007)MathSciNetGoogle Scholar
  6. 6.
    Brooks, D.M., Bose, P., Schuster, S.E., Jacobson, H., Kudva, P.N., Buyuktosunoglu, A., Wellman, J., 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.
    Carrasco, R.A., Iyengar, G., Stein, C.: Energy aware scheduling for weighted completion time and weighted tardiness. Technical report, (2011)Google Scholar
  8. 8.
    Chan, S.-H., Lam, T.-W., Lee, L.-K.: Non-clairvoyant speed scaling for weighted flow time. In: de Berg, M., Meyer, U. (eds.) ESA 2010, Part I. LNCS, vol. 6346, pp. 23–35. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Irani, S., Pruhs, K.R.: Algorithmic problems in power management. ACM SIGACT News 36(2), 63–76 (2005)CrossRefGoogle Scholar
  10. 10.
    Li, M.G., Yao, A.C., Yao, F.F.: Discrete and continuous min-energy schedules for variable voltage processor. Proceedings of the National Academy of Sciences of the United States of America, PNAS 2006 103, 3983–3987 (2006)CrossRefGoogle Scholar
  11. 11.
    Megow, N., Verschae, J.: Dual techniques for scheduling on a machine with varying speed. In: Fomin, F.V., Freivalds, R., Kwiatkowska, M., Peleg, D. (eds.) ICALP 2013, Part I. LNCS, vol. 7965, pp. 745–756. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  12. 12.
    Monderer, D., Shapley, L.S.: Potential games. Games and Economic Behavior 14, 124–143 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Moulin, H., Shenker, S.: Strategyproof sharing of submodular costs: budget balance versus efficiency. Economic Theory 18(3), 511–533 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Pruhs, K., Uthaisombut, P., Woeginger, G.: Getting the best response for your erg. ACM Transactions on Algorithms (TALG) 4(3), 38 (2008)MathSciNetGoogle Scholar
  15. 15.
    Vasquez, O.C.: Energy in computing systems with speed scaling: optimization and mechanisms design. Technical report, (2012)Google Scholar
  16. 16.
    Yao, F., Demers, A., Shenker, S.: A scheduling model for reduced cpu energy. In: Proceedings of the 36th Annual Symposium on Foundations of Computer Science, FOCS 1995, pp. 374–382. IEEE Computer Society, Washington, DC (1995)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Christoph Dürr
    • 1
  • Łukasz Jeż
    • 2
    • 3
  • Óscar C. Vásquez
    • 4
  1. 1.CNRS, LIP6Université Pierre et Marie CurieParisFrance
  2. 2.Institute of Computer ScienceUniversity of WrocławPoland
  3. 3.DIAGSapienza University of RomeItaly
  4. 4.LIP6 and Industrial Engineering DepartmentUniversity of Santiago of ChileChile

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