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Flow Time Minimization

  • Reference work entry
  • First Online:
Encyclopedia of Algorithms

  • 318 Accesses

Years and Authors of Summarized Original Work

  • 2001; Becchetti, Leonardi, Marchetti-Spaccamela, Pruhs

Problem Definition

Shortest-job-first heuristics arise in sequencing problems, when the goal is minimizing the perceived latency of users of a multiuser or multitasking system. In this problem, the algorithm has to schedule a set of jobs on a pool of m identical machines. Each job has a release date and a processing time, and the goal is to minimize the average time spent by jobs in the system. This is normally considered a suitable measure of the quality of service provided by a system to interactive users. This optimization problem can be more formally described as follows:

Input

A set of m identical machines and a set of n jobs \( { 1, 2,\ldots , n } \). Every job j has a release date r j and a processing time p j . In the sequel, \( { \mathcal{I} } \) denotes the set of feasible input instances.

Goal

The goal is minimizing the average flow (also known as average response) time of...

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Recommended Reading

  1. Becchetti L, Leonardi S (2004) Nonclairvoyant scheduling to minimize the total flow time on single and parallel machines. J ACM 51(4):517–539

    Article  MathSciNet  MATH  Google Scholar 

  2. Crovella ME, Frangioso R, Harchal-Balter M (1999) Connection scheduling in web servers. In: Proceedings of the 2nd USENIX symposium on Internet technologies and systems (USITS-99), pp 243–254

    Google Scholar 

  3. Kalyanasundaram B, Pruhs K (2003) Minimizing flow time nonclairvoyantly. J ACM 50(4):551–567

    Article  MathSciNet  MATH  Google Scholar 

  4. Kalyanasundaram B, Pruhs K (2000) Speed is as powerful as clairvoyance. J ACM 47(4):617–643

    Article  MathSciNet  MATH  Google Scholar 

  5. Kellerer H, Tautenhahn T, Woeginger GJ (1996) Approximability and nonapproximability results for minimizing total flow time on a singlemachine. In: Proceedings of 28th annual ACM symposium on the theory of computing (STOC'96), pp 418–426

    Google Scholar 

  6. Leonardi S, Raz D (1997) Approximating total flow time on parallel machines. In: Proceedings of the annual ACM symposium on the theory of computing (STOC), pp 110–119

    Google Scholar 

  7. Motwani R, Phillips S, Torng E (1994) Nonclairvoyant scheduling. Theor Comput Sci 130(1):17–47

    Article  MathSciNet  MATH  Google Scholar 

  8. Nutt G (1999) Operating system projects using Windows NT. Addison-Wesley, Reading

    Google Scholar 

  9. Schrage L (1968) A proof of the optimality of the shortest remaining processing time discipline. Oper Res 16(1):687–690

    Article  MathSciNet  MATH  Google Scholar 

  10. Smith DR (1976) A new proof of the optimality of the shortest remaining processing time discipline. Oper Res 26(1):197–199

    Article  MathSciNet  MATH  Google Scholar 

  11. Tanenbaum AS (1992) Modern operating systems. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Information and Computer Systems, University of Rome, Rome, Italy

    Luca Becchetti, Stefano Leonardi & Alberto Marchetti-Spaccamela

  2. Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, USA

    Kirk Pruhs

Authors
  1. Luca Becchetti
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  2. Stefano Leonardi
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  3. Alberto Marchetti-Spaccamela
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  4. Kirk Pruhs
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL, USA

    Ming-Yang Kao

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Becchetti, L., Leonardi, S., Marchetti-Spaccamela, A., Pruhs, K. (2016). Flow Time Minimization. In: Kao, MY. (eds) Encyclopedia of Algorithms. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2864-4_146

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