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A survey on makespan minimization in semi-online environments

Author:

Leah EpsteinAuthors Info & Claims

Journal of Scheduling, Volume 21, Issue 3

Pages 269 - 284

https://doi.org/10.1007/s10951-018-0567-z

Published: 01 June 2018 Publication History

Abstract

We discuss variants of online scheduling on identical and uniformly related machines, where the objective is to minimize the makespan. All variants are such that some information regarding the input is provided in advance, and therefore these models are known as semi-online problems. Algorithms are analyzed with respect to the competitive ratio. We discuss the benefit arising from different kinds of available information and find that almost all variants allow one to reduce the competitive ratio significantly compared to the best possible competitive ratio for the corresponding pure online problem.

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Cited By

Ge LWang ZBao WYuan DTran NZhou BZomaya A(2022)Semi-Online Multi-Machine with Restart Scheduling for Integrated Edge and Cloud Computing SystemsProceedings of the 51st International Conference on Parallel Processing10.1145/3545008.3545059(1-13)Online publication date: 29-Aug-2022
https://dl.acm.org/doi/10.1145/3545008.3545059
Zheng FChen YLiu MXu Y(2022)Competitive analysis of online machine rental and online parallel machine scheduling problems with workload fenceJournal of Combinatorial Optimization10.1007/s10878-022-00882-x44:2(1060-1076)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s10878-022-00882-x
Albers SGálvez WJanke M(2022)Machine Covering in the Random-Order ModelAlgorithmica10.1007/s00453-022-01011-085:6(1560-1585)Online publication date: 23-Jul-2022
https://dl.acm.org/doi/10.1007/s00453-022-01011-0
Show More Cited By

A survey on makespan minimization in semi-online environments
1. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis
    2. Online algorithms
      1. Online learning algorithms
  2. Theory and algorithms for application domains
    1. Machine learning theory
      1. Reinforcement learning

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cover image Journal of Scheduling

Journal of Scheduling Volume 21, Issue 3

June 2018

116 pages

ISSN:1094-6136

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Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

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Published: 01 June 2018

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Ge LWang ZBao WYuan DTran NZhou BZomaya A(2022)Semi-Online Multi-Machine with Restart Scheduling for Integrated Edge and Cloud Computing SystemsProceedings of the 51st International Conference on Parallel Processing10.1145/3545008.3545059(1-13)Online publication date: 29-Aug-2022
https://dl.acm.org/doi/10.1145/3545008.3545059
Zheng FChen YLiu MXu Y(2022)Competitive analysis of online machine rental and online parallel machine scheduling problems with workload fenceJournal of Combinatorial Optimization10.1007/s10878-022-00882-x44:2(1060-1076)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s10878-022-00882-x
Albers SGálvez WJanke M(2022)Machine Covering in the Random-Order ModelAlgorithmica10.1007/s00453-022-01011-085:6(1560-1585)Online publication date: 23-Jul-2022
https://dl.acm.org/doi/10.1007/s00453-022-01011-0
Balogh JBékési JDósa GEpstein LLevin A(2021)A New Lower Bound for Classic Online Bin PackingAlgorithmica10.1007/s00453-021-00818-783:7(2047-2062)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1007/s00453-021-00818-7
Albers SEckl A(2021)Scheduling with Testing on Multiple Identical Parallel MachinesAlgorithms and Data Structures10.1007/978-3-030-83508-8_3(29-42)Online publication date: 9-Aug-2021
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Balogh JBékési JDósa GEpstein LLevin A(2019)A New Lower Bound for Classic Online Bin PackingApproximation and Online Algorithms10.1007/978-3-030-39479-0_2(18-28)Online publication date: 12-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-39479-0_2

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