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Chaotic hybrid multi-objective optimization algorithm for scientific workflow scheduling in multisite clouds.

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Title: Chaotic hybrid multi-objective optimization algorithm for scientific workflow scheduling in multisite clouds.
Authors: Mohammadzadeh, Ali1 (AUTHOR) Ali.mohammadzadeh@iau.ac.ir, Javaheri, Danial2 (AUTHOR), Artin, Javad3 (AUTHOR)
Source: Journal of the Operational Research Society. Feb2024, Vol. 75 Issue 2, p314-335. 22p.
Subjects: Optimization algorithms, Metaheuristic algorithms, Workflow, Random numbers, Microsoft Azure
Abstract: A cloud is made up of many data centers, with its own set of data and resources. The reasons for employing several cloud sites to operate a workflow are that the data is already dispersed, the required resources surpass the constraints of a single site. This paper presents a hybrid multi-objective optimization algorithm denoted as HSOS-SOA, achieved by combining the Symbiotic Organisms Search and Seagull Optimization Algorithm. The HSOS-SOA uses chaotic maps to generate random numbers and performs a good trade-off between exploration and exploitation, resulting in a higher convergence rate. HSOS-SOA is used to solve scientific workflow scheduling problems in multisite cloud computing by taking into consideration elements such as makespan, cost, and reliability. A solution is chosen from the Pareto front using the knee-point approach in this approach. Extensive analyses are performed out in Microsoft Azure multisite cloud and the results exhibited that the HSOS-SOA can outperform other algorithms in terms of metrics such as IGD, Coverage Ratio, and so on. Experimental results of experiments reveal that the results in makespan improvement in the range of 5.72–28.61%, cost in the range of 5.16–45.16%, and reliability in the range of 3.11–25% over well-known metaheuristic algorithms. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the Operational Research Society is the property of Taylor & Francis Ltd 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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  Data: Chaotic hybrid multi-objective optimization algorithm for scientific workflow scheduling in multisite clouds.
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  Data: <searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Workflow%22">Workflow</searchLink><br /><searchLink fieldCode="DE" term="%22Random+numbers%22">Random numbers</searchLink><br /><searchLink fieldCode="DE" term="%22Microsoft+Azure%22">Microsoft Azure</searchLink>
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  Label: Abstract
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  Data: A cloud is made up of many data centers, with its own set of data and resources. The reasons for employing several cloud sites to operate a workflow are that the data is already dispersed, the required resources surpass the constraints of a single site. This paper presents a hybrid multi-objective optimization algorithm denoted as HSOS-SOA, achieved by combining the Symbiotic Organisms Search and Seagull Optimization Algorithm. The HSOS-SOA uses chaotic maps to generate random numbers and performs a good trade-off between exploration and exploitation, resulting in a higher convergence rate. HSOS-SOA is used to solve scientific workflow scheduling problems in multisite cloud computing by taking into consideration elements such as makespan, cost, and reliability. A solution is chosen from the Pareto front using the knee-point approach in this approach. Extensive analyses are performed out in Microsoft Azure multisite cloud and the results exhibited that the HSOS-SOA can outperform other algorithms in terms of metrics such as IGD, Coverage Ratio, and so on. Experimental results of experiments reveal that the results in makespan improvement in the range of 5.72–28.61%, cost in the range of 5.16–45.16%, and reliability in the range of 3.11–25% over well-known metaheuristic algorithms. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Journal of the Operational Research Society is the property of Taylor & Francis Ltd 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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      – Type: doi
        Value: 10.1080/01605682.2023.2195426
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 22
        StartPage: 314
    Subjects:
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Workflow
        Type: general
      – SubjectFull: Random numbers
        Type: general
      – SubjectFull: Microsoft Azure
        Type: general
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      – TitleFull: Chaotic hybrid multi-objective optimization algorithm for scientific workflow scheduling in multisite clouds.
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            NameFull: Mohammadzadeh, Ali
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            NameFull: Javaheri, Danial
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            NameFull: Artin, Javad
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            – D: 01
              M: 02
              Text: Feb2024
              Type: published
              Y: 2024
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