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Algorithms for Cost-Aware Scheduling

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Approximation and Online Algorithms (WAOA 2012)

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Introduction

In this paper, we generalize classical machine scheduling problems by introducing a cost involved in processing jobs, which varies as a function of time. Before defining the problems formally and discussing the technical novelty, we present a few technological motivations for introducing this model.

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References

  1. Albers, S.: Algorithms for energy saving. In: Albers, S., Alt, H., Näher, S. (eds.) Festschrift Mehlhorn. LNCS, vol. 5760, pp. 173–186. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  2. Albers, S., Fujiwara, H.: Energy-efficient algorithms for flow time minimization. ACM Transactions on Algorithms 3(4) (2007)

    Google Scholar 

  3. Bansal, N., Chan, H.-L., Pruhs, K.: Speed scaling with an arbitrary power function. In: SODA (2009)

    Google Scholar 

  4. Bansal, N., Chan, H.-L., Pruhs, K.: Competitive algorithms for due date scheduling. Algorithmica 59(4) (2011)

    Google Scholar 

  5. Bansal, N., Kimbrel, T., Pruhs, K.: Speed scaling to manage energy and temperature. J. ACM 54(1) (2007)

    Google Scholar 

  6. Bansal, N., Pruhs, K.: Server scheduling in the l p norm: A rising tide lifts all boat. In: Proceedings of the Thirty-Fifth Annual ACM Symposium on Theory of Computing, STOC (2003)

    Google Scholar 

  7. Bansal, N., Pruhs, K., Stein, C.: Speed scaling for weighted flow time. In: SODA (2007)

    Google Scholar 

  8. Becchetti, L., Leonardi, S., Marchetti-Spaccamela, A., Pruhs, K.R.: Online weighted flow time and deadline scheduling. In: Goemans, M.X., Jansen, K., Rolim, J.D.P., Trevisan, L. (eds.) APPROX-RANDOM 2001. LNCS, vol. 2129, pp. 36–47. Springer, Heidelberg (2001)

    Google Scholar 

  9. Chadha, J.S., Garg, N., Kumar, A., Muralidhara, V.N.: A competitive algorithm for minimizing weighted flow time on unrelated machines with speed augmentation. In: STOC (2009)

    Google Scholar 

  10. Chase, J.: Demand response for computing centers, http://www.cs.duke.edu/~chase/dr.pdf

  11. Epstein, L., Levin, A., Marchetti-Spaccamela, A., Megow, N., Mestre, J., Skutella, M., Stougie, L.: Universal sequencing on a single machine. In: Eisenbrand, F., Shepherd, F.B. (eds.) IPCO 2010. LNCS, vol. 6080, pp. 230–243. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  12. Gupta, A., Im, S., Krishnaswamy, R., Moseley, B., Pruhs, K.: Scheduling heterogeneous processors isn’t as easy as you think. In: SODA (2012)

    Google Scholar 

  13. Gupta, A., Krishnaswamy, R., Pruhs, K.: Scalably scheduling power-heterogeneous processors. In: Abramsky, S., Gavoille, C., Kirchner, C., Meyer auf der Heide, F., Spirakis, P.G. (eds.) ICALP 2010, Part I. LNCS, vol. 6198, pp. 312–323. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  14. Hall, L.A., Shmoys, D.B., Wein, J.: Scheduling to minimize average completion time: Off-line and on-line algorithms. In: SODA (1996)

    Google Scholar 

  15. Im, S., Moseley, B., Pruhs, K.: A tutorial on amortized local competitiveness in online scheduling. SIGACT News 42(2) (2011)

    Google Scholar 

  16. Im, S., Moseley, B., Pruhs, K.: Online scheduling with general cost functions. In: SODA (2012)

    Google Scholar 

  17. Irani, S., Shukla, S., Gupta, R.: Algorithms for power savings. ACM Trans. Algorithms 3(4) (November 2007)

    Google Scholar 

  18. Kalyanasundaram, B., Pruhs, K.: Speed is as powerful as clairvoyance. J. ACM 47 (July 2000)

    Google Scholar 

  19. Lam, T.-W., Lee, L.-K., Ting, H.-F., To, I.K.K., Wong, P.W.H.: Sleep with guilt and work faster to minimize flow plus energy. In: Albers, S., Marchetti-Spaccamela, A., Matias, Y., Nikoletseas, S., Thomas, W. (eds.) ICALP 2009, Part I. LNCS, vol. 5555, pp. 665–676. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  20. Murugesan, S.: Harnessing green it: Principles and practices. IT Professional 10(1) (2008)

    Google Scholar 

  21. Pruhs, K.: Green computing algorithmics. In: FOCS, pp. 3–4 (2011)

    Google Scholar 

  22. Pruhs, K., Stein, C.: How to schedule when you have to buy your energy. In: Serna, M., Shaltiel, R., Jansen, K., Rolim, J. (eds.) APPROX and RANDOM 2010. LNCS, vol. 6302, pp. 352–365. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  23. Pruhs, K., Uthaisombut, P., Woeginger, G.: Getting the best response for your erg. ACM Trans. Algorithms 4, 38:1–38:17 (2008)

    Article  MathSciNet  Google Scholar 

  24. Qureshi, A., Weber, R., Balakrishnan, H., Guttag, J., Maggs, B.: Cutting the electric bill for internet-scale systems. In: SIGCOMM (2009)

    Google Scholar 

  25. Electricity rates, http://www.pge.com/tariffs/electric.shtml

  26. Schmidt, G.: Scheduling with limited machine availability. European Journal of Operational Research 121, 1–15 (1998)

    Article  Google Scholar 

  27. Official Statistics. United States Department of Energy, http://www.eia.doe.gov

  28. Yao, F.F., Demers, A.J., Shenker, S.: A scheduling model for reduced cpu energy. In: FOCS (1995)

    Google Scholar 

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Kulkarni, J., Munagala, K. (2013). Algorithms for Cost-Aware Scheduling. In: Erlebach, T., Persiano, G. (eds) Approximation and Online Algorithms. WAOA 2012. Lecture Notes in Computer Science, vol 7846. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38016-7_17

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  • DOI: https://doi.org/10.1007/978-3-642-38016-7_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38015-0

  • Online ISBN: 978-3-642-38016-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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