Skip to main content

Non-clairvoyant Scheduling for Minimizing Mean Slowdown

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2607))

Abstract

We consider the problem of scheduling jobs online nonclairvoyantly, that is, when job sizes are not known. Our focus is on minimizing mean slowdown, defined as the ratio of flow time to the size of the job. We use resource augmentation in terms of allowing a faster processor to the online algorithm to make up for its lack of knowledge of job sizes.

Our main result is an O(1)-speed O(log2 B)-competitive algorithm for minimizing mean slowdown non-clairvoyantly, when B is the ratio between the largest and smallest job sizes. On the other hand, we show that any O(1)-speed algorithm, deterministic or randomized, is at least Ω(logB) competitive.

The motivation for bounded job sizes is supported by an .(n) lower bound for arbitrary job sizes, where n is the number of jobs. Furthermore, a lower bound of Ω(B) justifies the need for resource augmentation even with bounded job sizes. For the static case, i.e. when all jobs arrive at time 0, we give an O(logB) competitive algorithm which does not use resource augmentation and a matching Ω(logB) lower bound on the competitiveness.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. B. Awerbuch, Y. Azar, S. Leonardi, and O. Regev. Minimizing the flow time without migration. In ACM Symposium on Theory of Computing, pages 198–205, 1999.

    Google Scholar 

  2. N. Bansal and K. Dhamdhere. Minimizing weighted flow time. In 14th Annual ACM-SIAM Symposium on Discrete Algorithms, 2003.

    Google Scholar 

  3. L. Becchetti and S. Leonardi. Non-clairvoyant scheduling to minimize the average flow time on single and parallel machines. In ACM Symposium on Theory of Computing (STOC), pages 94–103, 2001.

    Google Scholar 

  4. L. Becchetti, S. Leonardi, and S. Muthukrishnan. Scheduling to minimize average stretch without migration. In Symposium on Discrete Algorithms, pages 548–557, 2000.

    Google Scholar 

  5. Luca Becchetti, Stefano Leonardi, Alberto Marchetti-Spaccamela, and Kirk R. Pruhs. Online weighted flow time and deadline scheduling. Lecture Notes in Computer Science, 2129:36–47, 2001.

    Google Scholar 

  6. M. Bender, S. Chakrabarti, and S. Muthukrishnan. Flow and stretch metrics for scheduling continuous job streams. In ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 270–279, 1998.

    Google Scholar 

  7. M. Bender, S. Muthukrishnan, and R. Rajaraman. Improved algorithms for stretch scheduling. In 13th Annual ACM-SIAM Symposium on Discrete Algorithms, 2002.

    Google Scholar 

  8. C. Chekuri, S. Khanna, and A. Zhu. Algorithms for weighted flow time. In ACM Symposium on Theory of Computing (STOC), 2001.

    Google Scholar 

  9. M. Crovella, R. Frangioso, and M. Harchol-Balter. Connection scheduling in web servers. In USENIX Symposium on Internet Technologies and Systems, 1999.

    Google Scholar 

  10. B. Kalyanasundaram and K. Pruhs. Speed is as powerful as clairvoyance. Journal of the ACM, 47(4):617–643, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  11. Bala Kalyanasundaram and Kirk Pruhs. Minimizing flow time nonclairvoyantly. In IEEE Symposium on Foundations of Computer Science, pages 345–352, 1997.

    Google Scholar 

  12. R. Motwani, S. Phillips, and E. Torng. Nonclairvoyant scheduling. Theoretical Computer Science, 130(1):17–47, 1994.

    Article  MATH  MathSciNet  Google Scholar 

  13. S. Muthukrishnan, R. Rajaraman, A. Shaheen, and J. Gehrke. Online scheduling to minimize average stretch. In IEEE Symposium on Foundations of Computer Science (FOCS), pages 433–442, 1999.

    Google Scholar 

  14. A. Silberschatz and P. Galvin. Operating System Concepts. Addison-Wesley, 5th Edition, 1998.

    Google Scholar 

  15. H. Zhu, B. Smith, and T. Yang. Scheduling optimization for resource-intensive web requests on server clusters. In ACM Symposium on Parallel Algorithms and Architectures, pages 13–22, 1999.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bansal, N., Dhamdhere, K., Könemann, J., Sinha, A. (2003). Non-clairvoyant Scheduling for Minimizing Mean Slowdown. In: Alt, H., Habib, M. (eds) STACS 2003. STACS 2003. Lecture Notes in Computer Science, vol 2607. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36494-3_24

Download citation

  • DOI: https://doi.org/10.1007/3-540-36494-3_24

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00623-7

  • Online ISBN: 978-3-540-36494-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics