Multi-swarm optimization model for multi-cloud scheduling for enhanced quality of services.
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| Title: | Multi-swarm optimization model for multi-cloud scheduling for enhanced quality of services. |
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| Authors: | Mohanraj, T.1 (AUTHOR) mohanrajt.me@gmail.com, Santhosh, R.1 (AUTHOR) |
| Source: | Soft Computing - A Fusion of Foundations, Methodologies & Applications. Dec2022, Vol. 26 Issue 23, p12985-12995. 11p. |
| Subjects: | Particle swarm optimization, Quality of service, Scheduling, Customer satisfaction, Cost functions, Cloud computing |
| Abstract: | Cloud services gain more attention due to its accessibility, performance, and cost factors. Cloud offers a wide range of services and completes the task without any delay due to its scheduling policies. Task scheduling is an important factor in cloud computing applications. The performance of applications increases due to an effective scheduling strategy. The cloud resources are allocated to the tasks through task scheduling. Factors like customer satisfaction, resource utilization, better performance make task scheduling crucial for service providers. Depending on the scheduling schemes support in clouds, scheduling is categorized into single cloud or multi-cloud scheduling. Multi-cloud environment provides diverse resources and significantly reduces the cost and commercial limitations. However, reducing the cost functions and makespan are the major factors considered to avoid customer dissatisfaction. But it is essential to concentrate on other factors, such as throughput, delay, Makespan, waiting time, response time, utilization, and efficiency to improve the quality of services. This research work presents a Multi-Swarm Optimization model for Multi-Cloud Scheduling for Enhanced Quality of Services for a multi-cloud environment. Experimental results demonstrate that the proposed approach performs better in all aspects compared to existing techniques, such as Adaptive energy-efficient scheduling, single objective particle swarm optimization scheduling, and improves the quality of services. [ABSTRACT FROM AUTHOR] |
| Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature 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 | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 159685456 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multi-swarm optimization model for multi-cloud scheduling for enhanced quality of services. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mohanraj%2C+T%2E%22">Mohanraj, T.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mohanrajt.me@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Santhosh%2C+R%2E%22">Santhosh, R.</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Soft+Computing+-+A+Fusion+of+Foundations%2C+Methodologies+%26+Applications%22">Soft Computing - A Fusion of Foundations, Methodologies & Applications</searchLink>. Dec2022, Vol. 26 Issue 23, p12985-12995. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+of+service%22">Quality of service</searchLink><br /><searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Customer+satisfaction%22">Customer satisfaction</searchLink><br /><searchLink fieldCode="DE" term="%22Cost+functions%22">Cost functions</searchLink><br /><searchLink fieldCode="DE" term="%22Cloud+computing%22">Cloud computing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Cloud services gain more attention due to its accessibility, performance, and cost factors. Cloud offers a wide range of services and completes the task without any delay due to its scheduling policies. Task scheduling is an important factor in cloud computing applications. The performance of applications increases due to an effective scheduling strategy. The cloud resources are allocated to the tasks through task scheduling. Factors like customer satisfaction, resource utilization, better performance make task scheduling crucial for service providers. Depending on the scheduling schemes support in clouds, scheduling is categorized into single cloud or multi-cloud scheduling. Multi-cloud environment provides diverse resources and significantly reduces the cost and commercial limitations. However, reducing the cost functions and makespan are the major factors considered to avoid customer dissatisfaction. But it is essential to concentrate on other factors, such as throughput, delay, Makespan, waiting time, response time, utilization, and efficiency to improve the quality of services. This research work presents a Multi-Swarm Optimization model for Multi-Cloud Scheduling for Enhanced Quality of Services for a multi-cloud environment. Experimental results demonstrate that the proposed approach performs better in all aspects compared to existing techniques, such as Adaptive energy-efficient scheduling, single objective particle swarm optimization scheduling, and improves the quality of services. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature 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: 10.1007/s00500-021-06184-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 12985 Subjects: – SubjectFull: Particle swarm optimization Type: general – SubjectFull: Quality of service Type: general – SubjectFull: Scheduling Type: general – SubjectFull: Customer satisfaction Type: general – SubjectFull: Cost functions Type: general – SubjectFull: Cloud computing Type: general Titles: – TitleFull: Multi-swarm optimization model for multi-cloud scheduling for enhanced quality of services. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mohanraj, T. – PersonEntity: Name: NameFull: Santhosh, R. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 14327643 Numbering: – Type: volume Value: 26 – Type: issue Value: 23 Titles: – TitleFull: Soft Computing - A Fusion of Foundations, Methodologies & Applications Type: main |
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