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Hybrid Electro search beetle optimization based task scheduling and game theory SOA based resource allocation in multi cloud computing.

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Title: Hybrid Electro search beetle optimization based task scheduling and game theory SOA based resource allocation in multi cloud computing.
Authors: Sreelatha, Gavini1 (AUTHOR) sreelathaprince13@gmail.com, Reddy, C. Kishor Kumar2 (AUTHOR) drckkreddy@gmail.com, Hanafiah, Marlia Mohd3,4 (AUTHOR) mhmarlia@ukm.edu.my, Madana Mohana, R.5 (AUTHOR) rmmnaidu@gmail.com
Source: Software: Practice & Experience. Feb2025, Vol. 55 Issue 2, p307-331. 25p.
Subjects: Hypervisor (Computer software), Time management, Metaheuristic algorithms, Resource allocation, Resource-based theory of the firm
Abstract: The most complicated process in multi‐cloud computing is resource allocation, as it needs to cope with a number of configurations and constraints of cloud providers and customers. At the time of resource allocation, the centralized cloud broker monitors the virtual machines (VM) status, scheduling process, and fitness. However, VM scheduling is found tedious and has received huge attention in business, academia, and research. This enhances the demand for both task scheduling and resource allocation in a multi‐cloud environment. To bridge the gap between the consumer requirement and server infrastructure, a joint optimization‐based resource allocation and task scheduling concept is analyzed in the proposed framework. The first phase introduces the task scheduling mechanism, which uses Hybrid Electro Search and Beetle Swarm Optimization to determine the optimal task for specific VMs. The optimal selection procedure is done by analyzing a multi‐cloud environment's makespan, energy, cost, and throughput parameters. In the second step, an Adaptive Game Theory‐based Seagull optimization approach performs several rounds of reassignment iteratively to minimize the variation in the expected completion time, consequently decreasing high energy consumption and load balancing. The experimental analysis for the proposed model is implemented using Python. The proposed methodology is shown to achieve cheaper costs, shorter waiting times, improved resource allocation, and efficient load balancing. Finally, a comparative analysis is performed with some hybrid optimization models, which illustrate the efficiency of the proposed hybrid optimization model. [ABSTRACT FROM AUTHOR]
Copyright of Software: Practice & Experience is the property of Wiley-Blackwell 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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  Data: Hybrid Electro search beetle optimization based task scheduling and game theory SOA based resource allocation in multi cloud computing.
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  Data: <searchLink fieldCode="DE" term="%22Hypervisor+%28Computer+software%29%22">Hypervisor (Computer software)</searchLink><br /><searchLink fieldCode="DE" term="%22Time+management%22">Time management</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Resource-based+theory+of+the+firm%22">Resource-based theory of the firm</searchLink>
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  Data: The most complicated process in multi‐cloud computing is resource allocation, as it needs to cope with a number of configurations and constraints of cloud providers and customers. At the time of resource allocation, the centralized cloud broker monitors the virtual machines (VM) status, scheduling process, and fitness. However, VM scheduling is found tedious and has received huge attention in business, academia, and research. This enhances the demand for both task scheduling and resource allocation in a multi‐cloud environment. To bridge the gap between the consumer requirement and server infrastructure, a joint optimization‐based resource allocation and task scheduling concept is analyzed in the proposed framework. The first phase introduces the task scheduling mechanism, which uses Hybrid Electro Search and Beetle Swarm Optimization to determine the optimal task for specific VMs. The optimal selection procedure is done by analyzing a multi‐cloud environment's makespan, energy, cost, and throughput parameters. In the second step, an Adaptive Game Theory‐based Seagull optimization approach performs several rounds of reassignment iteratively to minimize the variation in the expected completion time, consequently decreasing high energy consumption and load balancing. The experimental analysis for the proposed model is implemented using Python. The proposed methodology is shown to achieve cheaper costs, shorter waiting times, improved resource allocation, and efficient load balancing. Finally, a comparative analysis is performed with some hybrid optimization models, which illustrate the efficiency of the proposed hybrid optimization model. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Software: Practice & Experience is the property of Wiley-Blackwell 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: 10.1002/spe.3370
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        Text: English
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        PageCount: 25
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      – SubjectFull: Hypervisor (Computer software)
        Type: general
      – SubjectFull: Time management
        Type: general
      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Resource allocation
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      – SubjectFull: Resource-based theory of the firm
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      – TitleFull: Hybrid Electro search beetle optimization based task scheduling and game theory SOA based resource allocation in multi cloud computing.
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            NameFull: Sreelatha, Gavini
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            NameFull: Reddy, C. Kishor Kumar
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              M: 02
              Text: Feb2025
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              Y: 2025
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