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Hybrid electro search with genetic algorithm for task scheduling in cloud computing.

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Titel: Hybrid electro search with genetic algorithm for task scheduling in cloud computing.
Autoren: Velliangiri, S.1 (AUTHOR), Karthikeyan, P.2 (AUTHOR), Arul Xavier, V.M.3 (AUTHOR), Baswaraj, D.4 (AUTHOR)
Quelle: Ain Shams Engineering Journal. Mar2021, Vol. 12 Issue 1, p631-639. 9p.
Schlagwörter: Genetic algorithms, Search algorithms, Cloud computing, On-demand computing, Particle swarm optimization, Load balancing (Computer networks), Working class, Production scheduling
Abstract: Cloud computing is on-demand Internet-based computing, which is a highly scalable service adopted by different working and non-working classes of people around the globe. Task scheduling one of the critical applications used by end-users and cloud service providers. The significant challenging in the task scheduler is to find an optimal resource for the given input task. In this paper, we proposed Hybrid Electro Search with a genetic algorithm (HESGA) to improve the behavior of task scheduling by considering parameters such as makespan, load balancing, utilization of resources, and cost of the multi-cloud. The proposed method combined the advantage of a genetic algorithm and an electro search algorithm. The genetic algorithm provides the best local optimal solutions, whereas the Electro search algorithm provides the best global optima solutions. The proposed algorithm outperforms than existing scheduling algorithms such as Hybrid Particle Swarm Optimization Genetic Algorithm (HPSOGA), GA, ES, and ACO. [ABSTRACT FROM AUTHOR]
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  Data: Hybrid electro search with genetic algorithm for task scheduling in cloud computing.
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  Data: <searchLink fieldCode="JN" term="%22Ain+Shams+Engineering+Journal%22">Ain Shams Engineering Journal</searchLink>. Mar2021, Vol. 12 Issue 1, p631-639. 9p.
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  Data: Cloud computing is on-demand Internet-based computing, which is a highly scalable service adopted by different working and non-working classes of people around the globe. Task scheduling one of the critical applications used by end-users and cloud service providers. The significant challenging in the task scheduler is to find an optimal resource for the given input task. In this paper, we proposed Hybrid Electro Search with a genetic algorithm (HESGA) to improve the behavior of task scheduling by considering parameters such as makespan, load balancing, utilization of resources, and cost of the multi-cloud. The proposed method combined the advantage of a genetic algorithm and an electro search algorithm. The genetic algorithm provides the best local optimal solutions, whereas the Electro search algorithm provides the best global optima solutions. The proposed algorithm outperforms than existing scheduling algorithms such as Hybrid Particle Swarm Optimization Genetic Algorithm (HPSOGA), GA, ES, and ACO. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.asej.2020.07.003
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 9
        StartPage: 631
    Subjects:
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Search algorithms
        Type: general
      – SubjectFull: Cloud computing
        Type: general
      – SubjectFull: On-demand computing
        Type: general
      – SubjectFull: Particle swarm optimization
        Type: general
      – SubjectFull: Load balancing (Computer networks)
        Type: general
      – SubjectFull: Working class
        Type: general
      – SubjectFull: Production scheduling
        Type: general
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      – TitleFull: Hybrid electro search with genetic algorithm for task scheduling in cloud computing.
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            NameFull: Velliangiri, S.
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            NameFull: Karthikeyan, P.
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            NameFull: Arul Xavier, V.M.
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            NameFull: Baswaraj, D.
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            – D: 01
              M: 03
              Text: Mar2021
              Type: published
              Y: 2021
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              Value: 12
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            – TitleFull: Ain Shams Engineering Journal
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