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Speed Scaling with an Arbitrary Power Function

Published:01 March 2013Publication History
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Abstract

This article initiates a theoretical investigation into online scheduling problems with speed scaling where the allowable speeds may be discrete, and the power function may be arbitrary, and develops algorithmic analysis techniques for this setting. We show that a natural algorithm, which uses Shortest Remaining Processing Time for scheduling and sets the power to be one more than the number of unfinished jobs, is 3-competitive for the objective of total flow time plus energy. We also show that another natural algorithm, which uses Highest Density First for scheduling and sets the power to be the fractional weight of the unfinished jobs, is a 2-competitive algorithm for the objective of fractional weighted flow time plus energy.

References

  1. Albers, S. and Fujiwara, H. 2007. Energy-Efficient algorithms for flow time minimization. ACM Trans. Algor. 3, 4, 49. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Andrew, L. L., Wierman, A., and Tang, A. 2009. Optimal speed scaling under arbitrary power functions. SIGMETRICS Perform. Eval. Rev. 37, 2, 39--41. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Bansal, N. and Chan, H.-L. 2009. Weighted flow time does not admit O(1)-competitive algorithms. In Proceedings of the ACM-SIAM Symposium on Discrete Algorithms. 1238--1244. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Bansal, N., Chan, H.-L., Lam, T. W., and Lee, L.-K. 2008. Scheduling for speed bounded processors. In Proceedings of the International Colloquium on Automata, Languages and Programming. 409--420. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Bansal, N., Pruhs, K., and Stein, C. 2007. Speed scaling for weighted flow time. In Proceedings of the ACM-SIAM Symposium on Discrete Algorithms. 805--813. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Bower, F. A., Sorin, D. J., and Cox, L. P. 2008. The impact of dynamically heterogeneous multicore processors on thread scheduling. IEEE Micro 28, 3, 17--25. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Gupta, A., Krishnaswamy, R., and Pruhs, K. 2010a. Nonclairvoyantly scheduling power-heterogeneous processors. In Proceedings of the IEEE International Green Computing Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Gupta, A., Krishnaswamy, R., and Pruhs, K. 2010b. Scalably scheduling power-heterogeneous processors. In Proceedings of the International Colloquium on Automata Languages and Programming. 312--323. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Lam, T., Lee, L., To, I., and Wong, P. 2008. Speed scaling functions based for flow time scheduling based on active job count. In Proceedings of the European Symposium on Algorithms. 647--659. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Li, M. and Yao, F. F. 2005. An efficient algorithm for computing optimal discrete voltage schedules. SIAM J. Comput. 35, 658--671. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Pruhs, K. 2007. Competitive online scheduling for server systems. SIGMETRICS Perform. Eval. Rev. 34, 4, 52--58. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Pruhs, K., Uthaisombut, P., and Woeginger, G. J. 2008. Getting the best response for your erg. ACM Trans. Algor. 4, 3. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Transactions on Algorithms
      ACM Transactions on Algorithms  Volume 9, Issue 2
      March 2013
      89 pages
      ISSN:1549-6325
      EISSN:1549-6333
      DOI:10.1145/2438645
      Issue’s Table of Contents

      Copyright © 2013 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 1 March 2013
      • Accepted: 1 June 2012
      • Revised: 1 October 2011
      • Received: 1 November 2010
      Published in talg Volume 9, Issue 2

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