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Scheduling independent tasks to reduce mean finishing time

Authors:

J. Bruno,

E. G. Coffman, Jr.,

R. SethiAuthors Info & Claims

Communications of the ACM, Volume 17, Issue 7

Pages 382 - 387

https://doi.org/10.1145/361011.361064

Published: 01 July 1974 Publication History

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Abstract

Sequencing to minimize mean finishing time (or mean time in system) is not only desirable to the user, but it also tends to minimize at each point in time the storage required to hold incomplete tasks. In this paper a deterministic model of independent tasks is introduced and new results are derived which extend and generalize the algorithms known for minimizing mean finishing time. In addition to presenting and analyzing new algorithms it is shown that the most general mean-finishing-time problem for independent tasks is polynomial complete, and hence unlikely to admit of a non-enumerative solution.

References

[1]

Conway, R.W., Maxwell, W.L., and Miller, L.W. Theorv ol" Scheduling. Addison-Wesley, New York, 1900.

Google Scholar

[2]

Ford, L.R., and Fulkerson, D.R. Flows in Networks. Princeton U. Press, Princeton, N.J., 1962.

Google Scholar

[3]

Bruno, J. A scheduling algorithm for minimizing mean flow time. Tech. Rep. #141, Comput. Sci. Dept., Penn State U., University Park, Penna., July 1973.

Google Scholar

[4]

Cook, S.A. The complexity of theorem proving procedures. 3rd ACM Syrup. on Theory of Computing, Shaker Heights, Oh., May 1971, pp. 151-158.

Digital Library

Google Scholar

[5]

Karp, R.M. Reducibility between combinatorial problems. In Complexity o/Computer Computations, R.E. Milker and J.W. Thatcher (Eds) Plenum Press, New York 1972, pp. 85-103.

Google Scholar

[6]

Sahni, S. Some related problems from network flows, game theory and integer programming. 13th Ann. Syrup. on Switching and Automata Theory, Oct. 1972, pp. 130-139.

Digital Library

Google Scholar

[7]

Ullman, J.D. Polynomial complete scheduling problems TR9. Dept. of Comput. Sci., U. of California at Berkeley, Mar. 1973.

Google Scholar

[8]

Graham, R.L. Bounds for certain multiprocessing anomalies. Bell Syst. Tech J. (Nov. 1966), 1563-1581.

Crossref

Google Scholar

[9]

Graham, R.L. Bounds on multiprocessing timing anomalies. SIAM J. Appl. Math 17, 2 (Mar. 1969), 416-429.

Digital Library

Google Scholar

[10]

Coffman, E.G. On a conjecture concerning the comparison of SPT and LPT scheduling. Tech. Rep. #140, Comput. Sci. Dept., Penn State U., University Park, Penna., July 1973.

Google Scholar

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Fereidoonian FSadjadi SHeydari MMirzapour Al-e-Hashem S(2024)A timely efficient and emissions-aware multiobjective truck-sharing integrated scheduling model in container terminalsProceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment10.1177/14750902231225694238:4(982-1008)Online publication date: 7-Feb-2024
https://doi.org/10.1177/14750902231225694
Rault TSadi FBillaut JSoukhal A(2024)Scheduling two interfering job sets on identical parallel machines with makespan and total completion time minimizationJournal of Scheduling10.1007/s10951-024-00812-127:5(485-505)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s10951-024-00812-1
Ben Mokhtar SCanon LDugois AMarchal LRivière E(2024)A scheduling framework for distributed key-value stores and its application to tail latency minimizationJournal of Scheduling10.1007/s10951-023-00803-827:2(183-202)Online publication date: 1-Apr-2024
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Index Terms

Scheduling independent tasks to reduce mean finishing time
1. Computing methodologies
  1. Artificial intelligence
    1. Planning and scheduling
2. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis
      1. Scheduling algorithms
    2. Online algorithms
      1. Online learning algorithms
        Scheduling algorithms
  2. Theory and algorithms for application domains
    1. Machine learning theory
      1. Reinforcement learning
        Sequential decision making

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Optimal online scheduling algorithms are known for sporadic task systems scheduled upon a single processor. Additionally, optimal online scheduling algorithms are also known for restricted subclasses of sporadic task systems upon an identical ...
Scheduling independent tasks to reduce mean finishing-time (extended abstract)

In this paper we study the problem of scheduling a set of independent tasks on m ≥ 1 processors to minimize the mean finishing-time (mean time in system). The importance of the mean finishing-time criterion is that its minimization tends to reduce the ...

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Published In

Communications of the ACM Volume 17, Issue 7

July 1974

63 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/361011

Editor:
Robert L. Ashenhurst
Univ. of Chicago, Chicago, IL

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 July 1974

Published in CACM Volume 17, Issue 7

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Cited By

View all

Fereidoonian FSadjadi SHeydari MMirzapour Al-e-Hashem S(2024)A timely efficient and emissions-aware multiobjective truck-sharing integrated scheduling model in container terminalsProceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment10.1177/14750902231225694238:4(982-1008)Online publication date: 7-Feb-2024
https://doi.org/10.1177/14750902231225694
Rault TSadi FBillaut JSoukhal A(2024)Scheduling two interfering job sets on identical parallel machines with makespan and total completion time minimizationJournal of Scheduling10.1007/s10951-024-00812-127:5(485-505)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s10951-024-00812-1
Ben Mokhtar SCanon LDugois AMarchal LRivière E(2024)A scheduling framework for distributed key-value stores and its application to tail latency minimizationJournal of Scheduling10.1007/s10951-023-00803-827:2(183-202)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s10951-023-00803-8
Nip KXie P(2024)Computational complexity and algorithms for two scheduling problems under linear constraintsJournal of Combinatorial Optimization10.1007/s10878-024-01122-047:4Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1007/s10878-024-01122-0
Beauprez ECaron AMorge MRoutier J(2023)Délégation de lots de tâches pour la réduction de la durée moyenne de réalisationRevue Ouverte d'Intelligence Artificielle10.5802/roia.624:2(193-221)Online publication date: 4-Jul-2023
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Wang TWu DZhao W(2023)Minimizing Indirect Contacts in Urban Pick-Up and Delivery Services During COVID-19 PandemicIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2023.329993353:12(7876-7887)Online publication date: Dec-2023
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Lenstra JStrusevich VVlach M(2023)A historical note on the complexity of scheduling problemsOperations Research Letters10.1016/j.orl.2022.11.00651:1(1-2)Online publication date: Jan-2023
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Qamar RSarfraz MRahman AGhauri S(2023)Multi-criterion multi-UAV task allocation under dynamic conditionsJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.10173435:9(101734)Online publication date: Oct-2023
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Jiang XLee KPinedo M(2023)Approximation algorithms for bicriteria scheduling problems on identical parallel machines for makespan and total completion timeEuropean Journal of Operational Research10.1016/j.ejor.2022.06.021305:2(594-607)Online publication date: Mar-2023
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Gao JZhu XZhang R(2023)Scheduling for trial production with a parallel machine and multitasking scheduling modelApplied Intelligence10.1007/s10489-023-04845-553:22(26907-26926)Online publication date: 30-Aug-2023
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