Skip to main content

A Lower Bound for the On-Line Preemptive Machine Scheduling with ℓ p Norm

  • Conference paper
Computing and Combinatorics (COCOON 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5092))

Included in the following conference series:

  • 927 Accesses

Abstract

We consider the on-line version of the preemptive scheduling problem that minimizes the machine completion time vector in the ℓ p norm (a direct extension of the l  ∞  norm: the makespan) on m parallel identical machines. We present a lower bound on the competitive ratio of any randomized on-line algorithm with respect to the general ℓ p norm. This lower bound amounts to calculating a (non-convex) mathematical program and generalizes the existing result on makespan. While similar technique has been utilized to provide the best possible lower bound for makespan, the proposed lower bound failed to achieve the best possible lower bound for general ℓ p norm (though very close), and hence revealing intricate and essential difference between the general ℓ p norm and the makespan.

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

Access this chapter

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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alon, N., Azar, Y., Woeginger, G.J., Yadid, T.: Approximation Schemes for Scheduling. In: SODA 1997, pp. 493–500 (1997)

    Google Scholar 

  2. Avidor, A., Azar, Y., Sgall, J.: Ancient and New Algorithms for Load Balancing in the ℓ p Norm. Algorithmica 29, 422–441 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  3. Azar, Y., Epstein, A.: Convex programming for Scheduling Unrelated Parallel Machines. In: STOC 2005, pp. 331–337 (2005)

    Google Scholar 

  4. Azar, Y., Epstein, A., Epstein, L.: Load Balancing of Temporary Tasks in the ℓ p Norm. Theoretical Computer Science 361(2-3), 314–328 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  5. Azar, Y., Epstein, L., Richter, Y., Woeginger, G.J.: All-Norm Approximation Algorithms. In: Penttonen, M., Schmidt, E.M. (eds.) SWAT 2002. LNCS, vol. 2368, pp. 288–297. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  6. Azar, Y., Taub, S.: All-Norm Approximation for Scheduling on Identical Machines. In: Hagerup, T., Katajainen, J. (eds.) SWAT 2004. LNCS, vol. 3111, pp. 298–310. Springer, Heidelberg (2004)

    Google Scholar 

  7. Chandra, A.K., Wong, C.K.: Worst-Case Analysis of a Placement Algorithm Related to Storage Allocation. SIAM Journal on Computing 1, 249–263 (1975)

    Article  MathSciNet  Google Scholar 

  8. Chen, B., van Vliet, A., Woeginger, G.J.: An Optimal Algorithm for Preemptive On-Line Scheduling. Operations Research Letters 18(3), 127–131 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  9. Du, D.-L., Jiang, X., Zhang, G.: Optimal Preemptive Online Scheduling to Minimize ℓ p Norm on Two Processors. Journal of Manufacturing and Management Optimization 1(3), 345–351 (2005)

    MATH  MathSciNet  Google Scholar 

  10. Ebenlendr, T., Jawor, W., Sgall, J.: Preemptive Online Scheduling: Optimal Algorithms for All Speeds. In: Azar, Y., Erlebach, T. (eds.) ESA 2006. LNCS, vol. 4168, pp. 327–339. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  11. Epstein, L., Sgall, J.: A Lower Bound for On-Line Scheduling on Uniformly Related Machines. Operations Research Letters 26(1), 17–22 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  12. Epstein, L., Tassa, T.: Optimal Preemptive Scheduling for General Target Functions. Journal of Computer and System Sciences 72(1), 132–162 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  13. Kumar, V.S.A., Marathe, M.V., Parthasarathy, S., Srinivasan, A.: Approximation Algorithms for Scheduling on Multiple Machines. In: FOCS 2005, pp. 254–263 (2005)

    Google Scholar 

  14. Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G., Shmoys, D.B.: Sequencing and Scheduling: Algorithms and Complexity. In: Graves, S.C., Rinnooy Kan, A.H.G., Zipkin, P.H. (eds.) Logistics of Production and Inventory, pp. 445–522. North-Holland, Amsterdam (1993)

    Google Scholar 

  15. Lin, L.: Semi-Online Scheduling Algorithm under the ℓ p Norm on Two Identical Machines. Journal of Zhejiang University (Science Edition) 34(2), 148–151 (2007) (in Chinese)

    MathSciNet  MATH  Google Scholar 

  16. Lin, L., Tan, Z.Y., He, Y.: Deterministic and Randomized Scheduling Problems under the ℓ p Norm on Two Identical Machines. Journal of Zhejiang University Science 6(1), 20–26 (2005)

    Article  Google Scholar 

  17. McNaughton, R.: Scheduling with Deadlines and Loss Functions. Management Science 6(1), 1–12 (1959)

    Article  MATH  MathSciNet  Google Scholar 

  18. Tan, Z., He, Y., Epstein, L.: Optimal On-line Algorithms for the Uniform Machine Scheduling Problem with Ordinal Data. Information and Computation 196(1), 57–70 (2005)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Xiaodong Hu Jie Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Shuai, T., Du, D. (2008). A Lower Bound for the On-Line Preemptive Machine Scheduling with ℓ p Norm. In: Hu, X., Wang, J. (eds) Computing and Combinatorics. COCOON 2008. Lecture Notes in Computer Science, vol 5092. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69733-6_65

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-69733-6_65

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69732-9

  • Online ISBN: 978-3-540-69733-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics