GENETIC ANT COLONY ALGORITHM AND ITS DESIGN AND RESEARCH IN CLOUD COMPUTING PLATFORM RESOURCE SCHEDULING.
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| Title: | GENETIC ANT COLONY ALGORITHM AND ITS DESIGN AND RESEARCH IN CLOUD COMPUTING PLATFORM RESOURCE SCHEDULING. |
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| Authors: | DONGHUI MEI1 lrzhoujinyu@163.com, WENWEI SU1, YAN SHI1, YANXU JIN1 |
| Source: | Scalable Computing: Practice & Experience. Jul2025, Vol. 26 Issue 4, p1886-1894. 9p. |
| Subjects: | Simulated annealing, Computing platforms, Scheduling, Cloud computing, Ant algorithms |
| Abstract: | In order to solve the problems of slow convergence speed and low efficiency in finding precise solutions in existing cloud computing resource scheduling algorithms, the author proposes a genetic ant colony algorithm and its design and research in cloud computing platform resource scheduling. The author introduces a hybrid algorithm that integrates genetic algorithms with ant colony optimization. This approach begins by encoding parameters and seeks the best combination through evolutionary processes. It effectively merges the ant colony algorithm’s feedback mechanism with the genetic algorithm’s global search capabilities and rapid convergence. Then, multi-dimensional QoS constraints are proposed according to the needs of different users to perform local and global updates of pheromones. Finally, comparative simulation experiments were conducted on the cloud simulation platform CloudSim with simulated annealing algorithm (SA) and basic ant colony algorithm (ACO). The experimental results show that GAACO has a better time cost than ACO, but the time cost is longer than SA, and as the number of tasks increases, the time gap becomes larger. Compared with ACO, the time is reduced by 50.8%, and compared with SA, the time difference is 4%. Therefore, in terms of time cost, this algorithm is better than ACO. The algorithm proposed by the author effectively shortens the completion time of task scheduling, reduces operating costs, and has superior comprehensive performance. [ABSTRACT FROM AUTHOR] |
| Copyright of Scalable Computing: Practice & Experience is the property of Scalable Computing: Practice & Experience 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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 185633767 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: GENETIC ANT COLONY ALGORITHM AND ITS DESIGN AND RESEARCH IN CLOUD COMPUTING PLATFORM RESOURCE SCHEDULING. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22DONGHUI+MEI%22">DONGHUI MEI</searchLink><relatesTo>1</relatesTo><i> lrzhoujinyu@163.com</i><br /><searchLink fieldCode="AR" term="%22WENWEI+SU%22">WENWEI SU</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22YAN+SHI%22">YAN SHI</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22YANXU+JIN%22">YANXU JIN</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Scalable+Computing%3A+Practice+%26+Experience%22">Scalable Computing: Practice & Experience</searchLink>. Jul2025, Vol. 26 Issue 4, p1886-1894. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Simulated+annealing%22">Simulated annealing</searchLink><br /><searchLink fieldCode="DE" term="%22Computing+platforms%22">Computing platforms</searchLink><br /><searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Cloud+computing%22">Cloud computing</searchLink><br /><searchLink fieldCode="DE" term="%22Ant+algorithms%22">Ant algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In order to solve the problems of slow convergence speed and low efficiency in finding precise solutions in existing cloud computing resource scheduling algorithms, the author proposes a genetic ant colony algorithm and its design and research in cloud computing platform resource scheduling. The author introduces a hybrid algorithm that integrates genetic algorithms with ant colony optimization. This approach begins by encoding parameters and seeks the best combination through evolutionary processes. It effectively merges the ant colony algorithm’s feedback mechanism with the genetic algorithm’s global search capabilities and rapid convergence. Then, multi-dimensional QoS constraints are proposed according to the needs of different users to perform local and global updates of pheromones. Finally, comparative simulation experiments were conducted on the cloud simulation platform CloudSim with simulated annealing algorithm (SA) and basic ant colony algorithm (ACO). The experimental results show that GAACO has a better time cost than ACO, but the time cost is longer than SA, and as the number of tasks increases, the time gap becomes larger. Compared with ACO, the time is reduced by 50.8%, and compared with SA, the time difference is 4%. Therefore, in terms of time cost, this algorithm is better than ACO. The algorithm proposed by the author effectively shortens the completion time of task scheduling, reduces operating costs, and has superior comprehensive performance. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Scalable Computing: Practice & Experience is the property of Scalable Computing: Practice & Experience 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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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.12694/scpe.v26i4.4612 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 1886 Subjects: – SubjectFull: Simulated annealing Type: general – SubjectFull: Computing platforms Type: general – SubjectFull: Scheduling Type: general – SubjectFull: Cloud computing Type: general – SubjectFull: Ant algorithms Type: general Titles: – TitleFull: GENETIC ANT COLONY ALGORITHM AND ITS DESIGN AND RESEARCH IN CLOUD COMPUTING PLATFORM RESOURCE SCHEDULING. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: DONGHUI MEI – PersonEntity: Name: NameFull: WENWEI SU – PersonEntity: Name: NameFull: YAN SHI – PersonEntity: Name: NameFull: YANXU JIN IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 18951767 Numbering: – Type: volume Value: 26 – Type: issue Value: 4 Titles: – TitleFull: Scalable Computing: Practice & Experience Type: main |
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