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USE OF GENETIC ALGORITHMS FOR SCHEDULING JOBS IN LARGE SCALE GRID APPLICATIONS.

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Title: USE OF GENETIC ALGORITHMS FOR SCHEDULING JOBS IN LARGE SCALE GRID APPLICATIONS.
Alternate Title: GENETINIŲ ALGORITMŲ NAUDOJIMAS KOMPIUTERIŲ TINKLUOSE IR KALENDORINIS DARBŲ PLANAVIMAS.
Authors: Carretero, Javier1, Xhafa, Fatos1 fatos@lsi.upc.edu
Source: Technological & Economic Development of Economy. 2006, Vol. 12 Issue 1, p11-17. 7p.
Subjects: Genetic algorithms, Grid computing, Robust control, Algorithms, Mathematics
Abstract (English): In this paper we present the implementation of Genetic Algorithms (GA) for job scheduling on computational grids that optimizes the makespan and the total flowtime. Job scheduling on computational grids is a key problem in large scale grid-based applications for solving complex problems. The aim is to obtain an efficient scheduler able to allocate a large number of jobs originated from large scale applications to grid resources. Several variations for GA operators are examined in order to identify which works best for the problem. To this end we have developed a grid simulator package to generate large and very large size instances of the problem and have used them to study the performance of GA implementation. Through extensive experimenting and fine tuning of parameters we have identified the configuration of operators and parameters that outperforms the existing implementations in the literature for static instances of the problem. The experimental results show the robustness of the implementation, improved performance of static instances compared to reported results in the literature and, finally, a fast reduction of the makespan making thus the scheduler of practical interest for grid environments. [ABSTRACT FROM AUTHOR]
Abstract (Lithuanian): Aprašoma, kaip genetinis algoritmas taikomas darbų trukmėms optimizuoti kalendoriniam darbų planavimui, naudojant kompiuterių, sujungtų į tinklą, išteklius. Kalendorinis darbų planavimas, naudojant kompiuterių tinklą, yra aktuali problema, sprendžiant kompleksines, didelio masto problemas. Autorių tikslas – sukurti tokį algoritmą, kuris efektyviausiai paskirstytų teikiamų skaičiuoti darbų srautą į kompiuterių tinklą. Ištirti keli algoritmai, išrinktas geriausias. Sukurtas kompiuterių tinklo darbą imituojantis programinis paketas, jis patikrintas, sprendžiant konkrečius uždavinius. Eksperimentuojant rastas geriausias operatorių ir parametrų derinys, o eksperimento rezultatai atskleidė, jog darbų planavimo laikas sutrumpėjo. [ABSTRACT FROM AUTHOR]
Copyright of Technological & Economic Development of Economy is the property of Vilnius Gediminas Technical University 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.)
Database: Engineering Source
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DbLabel: Engineering Source
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  Label: Title
  Group: Ti
  Data: USE OF GENETIC ALGORITHMS FOR SCHEDULING JOBS IN LARGE SCALE GRID APPLICATIONS.
– Name: TitleAlt
  Label: Alternate Title
  Group: TiAlt
  Data: GENETINIŲ ALGORITMŲ NAUDOJIMAS KOMPIUTERIŲ TINKLUOSE IR KALENDORINIS DARBŲ PLANAVIMAS.
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  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Carretero%2C+Javier%22">Carretero, Javier</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Xhafa%2C+Fatos%22">Xhafa, Fatos</searchLink><relatesTo>1</relatesTo><i> fatos@lsi.upc.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Technological+%26+Economic+Development+of+Economy%22">Technological & Economic Development of Economy</searchLink>. 2006, Vol. 12 Issue 1, p11-17. 7p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Grid+computing%22">Grid computing</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+control%22">Robust control</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics%22">Mathematics</searchLink>
– Name: Abstract
  Label: Abstract (English)
  Group: Ab
  Data: In this paper we present the implementation of Genetic Algorithms (GA) for job scheduling on computational grids that optimizes the makespan and the total flowtime. Job scheduling on computational grids is a key problem in large scale grid-based applications for solving complex problems. The aim is to obtain an efficient scheduler able to allocate a large number of jobs originated from large scale applications to grid resources. Several variations for GA operators are examined in order to identify which works best for the problem. To this end we have developed a grid simulator package to generate large and very large size instances of the problem and have used them to study the performance of GA implementation. Through extensive experimenting and fine tuning of parameters we have identified the configuration of operators and parameters that outperforms the existing implementations in the literature for static instances of the problem. The experimental results show the robustness of the implementation, improved performance of static instances compared to reported results in the literature and, finally, a fast reduction of the makespan making thus the scheduler of practical interest for grid environments. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Lithuanian)
  Group: Ab
  Data: Aprašoma, kaip genetinis algoritmas taikomas darbų trukmėms optimizuoti kalendoriniam darbų planavimui, naudojant kompiuterių, sujungtų į tinklą, išteklius. Kalendorinis darbų planavimas, naudojant kompiuterių tinklą, yra aktuali problema, sprendžiant kompleksines, didelio masto problemas. Autorių tikslas – sukurti tokį algoritmą, kuris efektyviausiai paskirstytų teikiamų skaičiuoti darbų srautą į kompiuterių tinklą. Ištirti keli algoritmai, išrinktas geriausias. Sukurtas kompiuterių tinklo darbą imituojantis programinis paketas, jis patikrintas, sprendžiant konkrečius uždavinius. Eksperimentuojant rastas geriausias operatorių ir parametrų derinys, o eksperimento rezultatai atskleidė, jog darbų planavimo laikas sutrumpėjo. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Technological & Economic Development of Economy is the property of Vilnius Gediminas Technical University 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: 10.3846/13928619.2006.9637716
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 7
        StartPage: 11
    Subjects:
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Grid computing
        Type: general
      – SubjectFull: Robust control
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Mathematics
        Type: general
    Titles:
      – TitleFull: USE OF GENETIC ALGORITHMS FOR SCHEDULING JOBS IN LARGE SCALE GRID APPLICATIONS.
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            NameFull: Carretero, Javier
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            NameFull: Xhafa, Fatos
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            – D: 01
              M: 03
              Text: 2006
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              Y: 2006
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              Value: 12
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            – TitleFull: Technological & Economic Development of Economy
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