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Machine covering problem in MapReduce systems with a small number of machines.

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Τίτλος: Machine covering problem in MapReduce systems with a small number of machines.
Συγγραφείς: Zheng, Quanchang
Πηγή: Operational Research; Jun2026, Vol. 26 Issue 2, p1-18, 18p
Θεματικοί όροι: Production scheduling, Combinatorial optimization, Approximation algorithms, Computer programming, Processing (Computer program language), Mathematical optimization
Περίληψη: This study investigates machine covering problems on some uniform machines in the MapReduce system. The aim is to maximize the minimum machine completion time. Each job comprises both map tasks and reduce tasks. The map tasks of a job can be freely partitioned and scheduled to different machines for simultaneous processing, whereas reduce tasks can only proceed after all corresponding map tasks have been finished. The study involves both the preemptive and non-preemptive variants of the reduce tasks for the given problem. For the preemptive version of reduce tasks, we introduce optimal algorithms to scenarios involving two and three machines. For the non-preemptive version of reduce tasks, we design an approximation algorithm that guarantees a worse-case ratio of specifically for the two-machine case, where denotes the speed ratio between the two machines. [ABSTRACT FROM AUTHOR]
Copyright of Operational Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  – Url: https://dx.doi.org/doi:10.1007/s12351-025-01020-1
    Name: EDS - Springer Nature Journals (s7799221)
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DbLabel: Complementary Index
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  Label: Title
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  Data: Machine covering problem in MapReduce systems with a small number of machines.
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Zheng%2C+Quanchang%22">Zheng, Quanchang</searchLink>
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  Data: Operational Research; Jun2026, Vol. 26 Issue 2, p1-18, 18p
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  Data: <searchLink fieldCode="DE" term="%22Production+scheduling%22">Production scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Combinatorial+optimization%22">Combinatorial optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Approximation+algorithms%22">Approximation algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+programming%22">Computer programming</searchLink><br /><searchLink fieldCode="DE" term="%22Processing+%28Computer+program+language%29%22">Processing (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: This study investigates machine covering problems on some uniform machines in the MapReduce system. The aim is to maximize the minimum machine completion time. Each job comprises both map tasks and reduce tasks. The map tasks of a job can be freely partitioned and scheduled to different machines for simultaneous processing, whereas reduce tasks can only proceed after all corresponding map tasks have been finished. The study involves both the preemptive and non-preemptive variants of the reduce tasks for the given problem. For the preemptive version of reduce tasks, we introduce optimal algorithms to scenarios involving two and three machines. For the non-preemptive version of reduce tasks, we design an approximation algorithm that guarantees a worse-case ratio of specifically for the two-machine case, where denotes the speed ratio between the two machines. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Operational Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s12351-025-01020-1
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      – Code: eng
        Text: English
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      – SubjectFull: Production scheduling
        Type: general
      – SubjectFull: Combinatorial optimization
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      – SubjectFull: Approximation algorithms
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      – SubjectFull: Computer programming
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      – SubjectFull: Processing (Computer program language)
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      – SubjectFull: Mathematical optimization
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      – TitleFull: Machine covering problem in MapReduce systems with a small number of machines.
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              Text: Jun2026
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              Y: 2026
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