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An Online Algorithm Optimally Self-tuning to Congestion for Power Management Problems

  • Wolfgang Bein
  • Naoki Hatta
  • Nelson Hernandez-Cons
  • Hiro Ito
  • Shoji Kasahara
  • Jun Kawahara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7164)

Abstract

We consider the classical power management problem: There is a device which has two states ON and OFF and one has to develop a control algorithm for changing between these states as to minimize (energy) cost when given a sequence of service requests. Although an optimal 2-competitive algorithm exists, that algorithm does not have good performance in many practical situations, especially in case the device is not used frequently. To take the frequency of device usage into account, we construct an algorithm based on the concept of “slackness degree.” Then by relaxing the worst case competitive ratio of our online algorithm to 2 + ε, where ε is an arbitrary small constant, we make the algorithm flexible to slackness. The algorithm thus automatically tunes itself to slackness degree and gives better performance than the optimal 2-competitive algorithm for real world inputs. In addition to worst case competitive ratio analysis, a queueing model analysis is given and computer simulations are reported, confirming that the performance of the algorithm is high.

Keywords

Switching Cost Competitive Ratio Online Algorithm Busy Period Sleep Mode 
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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References

  1. 1.
    Augustine, J., Irani, S., Swamy, C.: Optimal power-down strategies. In: Proc. 45th Symp. Foundations of Computer Science (FOCS), pp. 530–539. IEEE (2004)Google Scholar
  2. 2.
    Ben-David, S., Borodin, A.: A new measure for the study of on-line algorithms. Algorithmica 11, 73–91 (1994)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Borodin, A., Irani, S., Raghavan, P., Schieber, B.: Competitive paging with locality of reference. J. Comput. Systems Sci. 50, 244–258 (1995)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Boyar, J., Krarup, S., Nielsen, M.N.: Seat reservation allowing seat changes. J. Algorithms 52, 169–192 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Chrobak, M.: Sigact news online algorithms column 8. SIGACT News 36, 67–81 (2005)CrossRefGoogle Scholar
  6. 6.
    Chung, E., Benini, L., Bogliolo, A.: Dynamic power management for non-stationary service requests. In: Proceedings of the Design and Automation and Test in Europe Conference and Exhibition, pp. 77–81 (1999)Google Scholar
  7. 7.
    Eggers, S.J., Katz, R.H.: Evaluating the performance of four snooping cache coherency protocols. In: Proc. 16th International Symp. on Computer Architecture (ISCA). IEEE (1989)Google Scholar
  8. 8.
    Irani, S., Gupta, R., Shukla, S.: Competitive analysis of dynamic power management strategies for systems with multiple power savings states. In: DATE 2002: Proceedings of the Conference on Design, Automation and Test in Europe, p. 117. IEEE Computer Society, Washington, DC, USA (2002)CrossRefGoogle Scholar
  9. 9.
    Irani, S., Pruhs, K.R.: Algorithmic problems in power management. ACM SIGACT News (2005)Google Scholar
  10. 10.
    Karlin, A.R., Kenyon, C., Randall, D.: Dynamic tcp acknowledgement and other stories about e/(e − 1). In: Proc. 33rd STOC, pp. 502–509. ACM (2001)Google Scholar
  11. 11.
    Karlin, A., Manasse, M., McGeoch, L., Owicki, S.: Competitive randomized algorithms for nonuniform problems. Algorithmica 11, 542–571 (1994)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Karlin, A., Manasse, M., Rudolph, L., Sleator, D.: Competitive snoopy caching. Algorithmica 3, 79–119 (1988)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Kenyon, C.: Best-fit bin-packing with random order. In: Proc. 7th Symp. on Discrete Algorithms (SODA), pp. 359–364. ACM/SIAM (1996)Google Scholar
  14. 14.
    Koutsoupias, E., Papadimitriou, C.: Beyond competitive analysis. SIAM J. Comput. 30, 300–317 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Lotker, Z., Patt-Shamir, B., Rawitz, D.: Rent, lease or buy: Randomized algorithms for multislope ski rental. In: Albers, S., Weil, P. (eds.) 25th International Symposium on Theoretical Aspects of Computer Science (STACS 2008). Leibniz International Proceedings in Informatics (LIPIcs), vol. 1, pp. 503–514. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany (2008)Google Scholar
  16. 16.
    Panagiotou, K., Souza, A.: On adequate performance measures for paging. In: Proceedings of the Thirty-Eighth Annual ACM Symposium on Theory of Computing, STOC 2006, pp. 487–496. ACM, New York (2006)CrossRefGoogle Scholar
  17. 17.
    Phillips, S., Westbrook, J.: Competitive analysis and beyond. In: Algorithms and Theory of Computation Handbook, ch.10. CRC Press (1999)Google Scholar
  18. 18.
    Ramanathan, D., Irani, S., Gupta, R.: Latency effects of system level power management algorithms. In: Proceedings of the IEEE International Conference on Computer Aided Design (2000)Google Scholar
  19. 19.
    Wolff, R.W.: Stochastic modeling and the theory of queues. Prentice-Hall (1989)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Wolfgang Bein
    • 1
  • Naoki Hatta
    • 2
  • Nelson Hernandez-Cons
    • 3
  • Hiro Ito
    • 2
  • Shoji Kasahara
    • 3
  • Jun Kawahara
    • 4
  1. 1.Center for Information Technology and Algorithms, School of Computer ScienceUniversity of NevadaLas VegasUSA
  2. 2.Department of Communications and Computer Engineering, Graduate School of InformaticsKyoto UniversityJapan
  3. 3.Department of Systems Science, Graduate School of InformaticsKyoto UniversityJapan
  4. 4.JST ERATO MINATO ProjectJapan

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