An optimized cost-based data allocation model for heterogeneous distributed computing systems.
Saved in:
| Title: | An optimized cost-based data allocation model for heterogeneous distributed computing systems. |
|---|---|
| Authors: | Tarun, Sashi1 sashitarun79@gmail.com, Dubey, Mithilesh Kumar1 mithilesh.21436@lpu.co.in, Batth, Ranbir Singh1 ranbir.21123@lpu.co.in, Kaur, Sukhpreet2 sukhpreet.4479@cgc.edu.in |
| Source: | International Journal of Electrical & Computer Engineering (2088-8708). Dec2022, Vol. 12 Issue 6, p6373-6386. 14p. |
| Subjects: | Heterogeneous distributed computing, Swarm intelligence, Directed acyclic graphs, Architectural models |
| Abstract: | Continuous attempts have been made to improve the flexibility and effectiveness of distributed computing systems. Extensive effort in the fields of connectivity technologies, network programs, high processing components, and storage helps to improvise results. However, concerns such as slowness in response, long execution time, and long completion time have been identified as stumbling blocks that hinder performance and require additional attention. These defects increased the total system cost and made the data allocation procedure for a geographically dispersed setup difficult. The load-based architectural model has been strengthened to improve data allocation performance. To do this, an abstract job model is employed, and a data query file containing input data is processed on a directed acyclic graph. The jobs are executed on the processing engine with the lowest execution cost, and the system's total cost is calculated. The total cost is computed by summing the costs of communication, computation, and network. The total cost of the system will be reduced using a Swarm intelligence algorithm. In heterogeneous distributed computing systems, the suggested approach attempts to reduce the system's total cost and improve data distribution. According to simulation results, the technique efficiently lowers total system cost and optimizes partitioned data allocation. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science 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 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 159765585 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: An optimized cost-based data allocation model for heterogeneous distributed computing systems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tarun%2C+Sashi%22">Tarun, Sashi</searchLink><relatesTo>1</relatesTo><i> sashitarun79@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Dubey%2C+Mithilesh+Kumar%22">Dubey, Mithilesh Kumar</searchLink><relatesTo>1</relatesTo><i> mithilesh.21436@lpu.co.in</i><br /><searchLink fieldCode="AR" term="%22Batth%2C+Ranbir+Singh%22">Batth, Ranbir Singh</searchLink><relatesTo>1</relatesTo><i> ranbir.21123@lpu.co.in</i><br /><searchLink fieldCode="AR" term="%22Kaur%2C+Sukhpreet%22">Kaur, Sukhpreet</searchLink><relatesTo>2</relatesTo><i> sukhpreet.4479@cgc.edu.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Dec2022, Vol. 12 Issue 6, p6373-6386. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Heterogeneous+distributed+computing%22">Heterogeneous distributed computing</searchLink><br /><searchLink fieldCode="DE" term="%22Swarm+intelligence%22">Swarm intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Directed+acyclic+graphs%22">Directed acyclic graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Architectural+models%22">Architectural models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Continuous attempts have been made to improve the flexibility and effectiveness of distributed computing systems. Extensive effort in the fields of connectivity technologies, network programs, high processing components, and storage helps to improvise results. However, concerns such as slowness in response, long execution time, and long completion time have been identified as stumbling blocks that hinder performance and require additional attention. These defects increased the total system cost and made the data allocation procedure for a geographically dispersed setup difficult. The load-based architectural model has been strengthened to improve data allocation performance. To do this, an abstract job model is employed, and a data query file containing input data is processed on a directed acyclic graph. The jobs are executed on the processing engine with the lowest execution cost, and the system's total cost is calculated. The total cost is computed by summing the costs of communication, computation, and network. The total cost of the system will be reduced using a Swarm intelligence algorithm. In heterogeneous distributed computing systems, the suggested approach attempts to reduce the system's total cost and improve data distribution. According to simulation results, the technique efficiently lowers total system cost and optimizes partitioned data allocation. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=159765585 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.11591/ijece.v12i6.pp6373-6386 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 6373 Subjects: – SubjectFull: Heterogeneous distributed computing Type: general – SubjectFull: Swarm intelligence Type: general – SubjectFull: Directed acyclic graphs Type: general – SubjectFull: Architectural models Type: general Titles: – TitleFull: An optimized cost-based data allocation model for heterogeneous distributed computing systems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tarun, Sashi – PersonEntity: Name: NameFull: Dubey, Mithilesh Kumar – PersonEntity: Name: NameFull: Batth, Ranbir Singh – PersonEntity: Name: NameFull: Kaur, Sukhpreet IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 20888708 Numbering: – Type: volume Value: 12 – Type: issue Value: 6 Titles: – TitleFull: International Journal of Electrical & Computer Engineering (2088-8708) Type: main |
| ResultId | 1 |
