Maximizing job benefits on-line

Baruch Awerbuch, Yossi Azar, Oded Regev

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider a benefit model for on-line preemptive scheduling. In this model jobs arrive to the on-line scheduler at their release time. Each job arrives with its own execution time and its benefit function. The flow time of a job is the time that passes from its release to its completion. The benefit function specifies the benefit gained for any given flow time. A scheduler’s goal is to maximize the total gained benefit. We present a constant competitive ratio algorithm for that model in the uniprocessor case for benefit functions that do not decrease too fast. We also extend the algorithm to the multiprocessor case while maintaining constant competitiveness. The multiprocessor algorithm does not use migration, i.e., preempted jobs continue their execution on the same processor on which they were originally processed.

Original languageEnglish (US)
Title of host publicationApproximation Algorithms for Combinatorial Optimization - 3rd International Workshop, APPROX 2000, Proceedings
PublisherSpringer Verlag
Pages42-50
Number of pages9
Volume1913
ISBN (Print)9783540679967
StatePublished - 2000
Event3rd International Workshop on Approximation Algorithms for Combinatorial Optimization, APPROX 2000 - Saarbrucken, Germany
Duration: Sep 5 2000Sep 8 2000

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1913
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other3rd International Workshop on Approximation Algorithms for Combinatorial Optimization, APPROX 2000
CountryGermany
CitySaarbrucken
Period9/5/009/8/00

Fingerprint

Flow Time
Multiprocessor
Scheduler
Scheduling
Preemptive Scheduling
Release Time
Competitive Ratio
Competitiveness
Execution Time
Migration
Completion
Continue
Maximise
Model
Decrease

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Awerbuch, B., Azar, Y., & Regev, O. (2000). Maximizing job benefits on-line. In Approximation Algorithms for Combinatorial Optimization - 3rd International Workshop, APPROX 2000, Proceedings (Vol. 1913, pp. 42-50). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1913). Springer Verlag.

Maximizing job benefits on-line. / Awerbuch, Baruch; Azar, Yossi; Regev, Oded.

Approximation Algorithms for Combinatorial Optimization - 3rd International Workshop, APPROX 2000, Proceedings. Vol. 1913 Springer Verlag, 2000. p. 42-50 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1913).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Awerbuch, B, Azar, Y & Regev, O 2000, Maximizing job benefits on-line. in Approximation Algorithms for Combinatorial Optimization - 3rd International Workshop, APPROX 2000, Proceedings. vol. 1913, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1913, Springer Verlag, pp. 42-50, 3rd International Workshop on Approximation Algorithms for Combinatorial Optimization, APPROX 2000, Saarbrucken, Germany, 9/5/00.
Awerbuch B, Azar Y, Regev O. Maximizing job benefits on-line. In Approximation Algorithms for Combinatorial Optimization - 3rd International Workshop, APPROX 2000, Proceedings. Vol. 1913. Springer Verlag. 2000. p. 42-50. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Awerbuch, Baruch ; Azar, Yossi ; Regev, Oded. / Maximizing job benefits on-line. Approximation Algorithms for Combinatorial Optimization - 3rd International Workshop, APPROX 2000, Proceedings. Vol. 1913 Springer Verlag, 2000. pp. 42-50 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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