ENHANCED THROTTLED LOAD BALANCING FOR VIRTUAL MACHINE ALLOCATION IN MULTIPLE DATA CENTERS.
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| Title: | ENHANCED THROTTLED LOAD BALANCING FOR VIRTUAL MACHINE ALLOCATION IN MULTIPLE DATA CENTERS. |
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| Authors: | RAO, P. HANUMANTHA1 hanumanthraovbit@gmail.com, RAJAKUMAR, P. S.2 Rajakumar.subramanian@drmgrdu.ac.in |
| Source: | Scalable Computing: Practice & Experience. Sep2024, Vol. 25 Issue 5, p3453-3467. 15p. |
| Subjects: | Virtual machine systems, Data processing service centers, Industrial robots, Information technology industry, Software as a service, Cloud computing |
| Abstract: | "Cloud computing" hosts software and other services in remote data centers that customers can access worldwide. The user may access all the services and applications online. The IT industry has benefited greatly from the proliferation of cloud computing. On the flip side, organizations moved their operations to the cloud as a result of industrial automation. A surge in demand for cloud computing was directly correlated to the quick migration of businesses. Businesses looking to minimize expenses without sacrificing service quality will find this approach to be ideal. Considering the meteoric rise of cloud computing, service providers are delighted. Contrarily, distributing resources is a challenging task. Cloud computing overcomes some of its most fundamental obstacles, one of which is the load-balancing approach employed by load-balancers to economically optimize costs while minimizing time expenditures. Quick services for cloud customers and minimal cost for cloud providers are the goals of the optimal resource allocation method. This research suggests a novel approach to increase task processing time, which can aid in increasing cloud computing's load balancing capabilities. The proposed method Enhanced Throttled Load Balancing Algorithm (ETLBA) is an upgrade to the original Throttled Algorithm, which efficiently performs resource allocation and load balancing. The proposed ETLBA is contrasted with the existing algorithms, Round Robin, Active Monitoring Load Balancing Algorithm (AMLBA) and Throttled Load Balancing Algorithm (TLBA) to display the efficacy. Cloud Analyst tool simulates the proposed and existing methods. According on the results of the simulations, the proposed algorithm ETLBA achieves better outcomes than the popular existing algorithms in terms of processing time, request processing time, and datacenter cost. It shows 18% reduction in response time, 7% reduction in data center processing time, 16% reduction in data center request processing time and 4% less data center cost compared to the existing solutions. ETLBA performs better by selecting virtual machines using a prioritized index table and consumption index. It limits idling resources, improves response as well as reduces processing times, and cloud costs compared to conventional solutions. [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: 178841740 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: ENHANCED THROTTLED LOAD BALANCING FOR VIRTUAL MACHINE ALLOCATION IN MULTIPLE DATA CENTERS. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22RAO%2C+P%2E+HANUMANTHA%22">RAO, P. HANUMANTHA</searchLink><relatesTo>1</relatesTo><i> hanumanthraovbit@gmail.com</i><br /><searchLink fieldCode="AR" term="%22RAJAKUMAR%2C+P%2E+S%2E%22">RAJAKUMAR, P. S.</searchLink><relatesTo>2</relatesTo><i> Rajakumar.subramanian@drmgrdu.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Scalable+Computing%3A+Practice+%26+Experience%22">Scalable Computing: Practice & Experience</searchLink>. Sep2024, Vol. 25 Issue 5, p3453-3467. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Virtual+machine+systems%22">Virtual machine systems</searchLink><br /><searchLink fieldCode="DE" term="%22Data+processing+service+centers%22">Data processing service centers</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+robots%22">Industrial robots</searchLink><br /><searchLink fieldCode="DE" term="%22Information+technology+industry%22">Information technology industry</searchLink><br /><searchLink fieldCode="DE" term="%22Software+as+a+service%22">Software as a service</searchLink><br /><searchLink fieldCode="DE" term="%22Cloud+computing%22">Cloud computing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: "Cloud computing" hosts software and other services in remote data centers that customers can access worldwide. The user may access all the services and applications online. The IT industry has benefited greatly from the proliferation of cloud computing. On the flip side, organizations moved their operations to the cloud as a result of industrial automation. A surge in demand for cloud computing was directly correlated to the quick migration of businesses. Businesses looking to minimize expenses without sacrificing service quality will find this approach to be ideal. Considering the meteoric rise of cloud computing, service providers are delighted. Contrarily, distributing resources is a challenging task. Cloud computing overcomes some of its most fundamental obstacles, one of which is the load-balancing approach employed by load-balancers to economically optimize costs while minimizing time expenditures. Quick services for cloud customers and minimal cost for cloud providers are the goals of the optimal resource allocation method. This research suggests a novel approach to increase task processing time, which can aid in increasing cloud computing's load balancing capabilities. The proposed method Enhanced Throttled Load Balancing Algorithm (ETLBA) is an upgrade to the original Throttled Algorithm, which efficiently performs resource allocation and load balancing. The proposed ETLBA is contrasted with the existing algorithms, Round Robin, Active Monitoring Load Balancing Algorithm (AMLBA) and Throttled Load Balancing Algorithm (TLBA) to display the efficacy. Cloud Analyst tool simulates the proposed and existing methods. According on the results of the simulations, the proposed algorithm ETLBA achieves better outcomes than the popular existing algorithms in terms of processing time, request processing time, and datacenter cost. It shows 18% reduction in response time, 7% reduction in data center processing time, 16% reduction in data center request processing time and 4% less data center cost compared to the existing solutions. ETLBA performs better by selecting virtual machines using a prioritized index table and consumption index. It limits idling resources, improves response as well as reduces processing times, and cloud costs compared to conventional solutions. [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.v25i5.3165 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 3453 Subjects: – SubjectFull: Virtual machine systems Type: general – SubjectFull: Data processing service centers Type: general – SubjectFull: Industrial robots Type: general – SubjectFull: Information technology industry Type: general – SubjectFull: Software as a service Type: general – SubjectFull: Cloud computing Type: general Titles: – TitleFull: ENHANCED THROTTLED LOAD BALANCING FOR VIRTUAL MACHINE ALLOCATION IN MULTIPLE DATA CENTERS. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: RAO, P. HANUMANTHA – PersonEntity: Name: NameFull: RAJAKUMAR, P. S. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 18951767 Numbering: – Type: volume Value: 25 – Type: issue Value: 5 Titles: – TitleFull: Scalable Computing: Practice & Experience Type: main |
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