Advertisement

The Worst Page-Replacement Policy

  • Kunal Agrawal
  • Michael A. Bender
  • Jeremy T. Fineman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4475)

Abstract

In this paper, we consider the question: what is the worst possible page-replacement strategy? Our goal is to devise an online strategy that has the highest possible fraction of misses as compared to the worst offline strategy. We show that there is no deterministic, online page-replacement strategy that is competitive with the worst offline strategy. We give a randomized strategy based on the “most-recently-used” heuristic, and show that this is the worst possible online page-replacement strategy.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Achlioptas, D., Chrobak, M., Noga, J.: Competitive analysis of randomized paging algorithms. In: Díaz, J. (ed.) ESA 1996. LNCS, vol. 1136, pp. 419–430. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  2. 2.
    Belady, L.A.: A study of replacement algorithms for virtual storage computers. IBM Systems Journal 5(2), 78–101 (1966)CrossRefGoogle Scholar
  3. 3.
    Borodin, A., Irani, S., Raghavan, P., Schieber, B.: Competitive paging with locality of reference. Journal of Computer and System Sciences 50(2), 244–258 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Fiat, A., Karp, R.M., Luby, M., McGeoch, L.A., Sleator, D.D., Young, N.E.: Competitive paging algorithms. Journal of Algorithms 12(4), 685–699 (1991)CrossRefzbMATHGoogle Scholar
  5. 5.
    Frigo, M., Leiserson, C.E., Prokop, H., Ramachandran, S.:Cache-oblivious algorithms. In: 40th Annual Symposium on Foundations of Computer Science, pp. 285–297, New York, October 17–19 (1999)Google Scholar
  6. 6.
    Hennessy, J.L., Patterson, D.A.: Computer Architecture: a Quantitative Approach, 3rd edn. Morgan Kaufmann, San Francisco, CA (2003)zbMATHGoogle Scholar
  7. 7.
    Irani, S.: Competitive analysis of paging. In: Fiat, A. (ed.) Developments from a June 1996 Seminar on Online Algorithms. LNCS, vol. 1442, pp. 52–73. Springer, Heidelberg (1998)Google Scholar
  8. 8.
    McGeoch, L.A., Sleator, D.D.: A strongly competitive randomized paging algorithm. Algorithmica 6, 816–825 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Sandeep Sen and Siddhartha Chatterjee. Towards a theory of cache-efficient algorithms. In: Proceedings of the 11th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 829–838, San Francisco, California (January 2000)Google Scholar
  10. 10.
    Sleator, D.D., Tarjan, R.E.: Amortized efficiency of list update and paging rules. Communications of the ACM 28(2), 202–208 (1985)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Vitter, J.S.: Random sampling with a reservoir. ACM Transactions on Mathematical Software 11(1), 37–57 (1985)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Kunal Agrawal
    • 1
  • Michael A. Bender
    • 2
  • Jeremy T. Fineman
    • 1
  1. 1.MIT, Cambridge, MA 02139USA
  2. 2.Stony Brook University, Stony Brook, NY 11794-4400USA

Personalised recommendations