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A Dynamic Resource-Aware Load Balancing Approach for Optimized Performance in Cloud Computing.

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Title: A Dynamic Resource-Aware Load Balancing Approach for Optimized Performance in Cloud Computing.
Authors: Mehta, Sunaina1 sunainabagga@rimt.ac.in, Bhardwaj, Sushil1
Source: International Journal of Performability Engineering. Feb2026, Vol. 22 Issue 2, p110-118. 9p.
Subjects: Cloud computing, Load balancing (Computer networks), Resource management, Resource allocation, Scalability, Central processing units, Enterprise resource planning software, Simulation methods & models
Abstract: Cloud computing has emerged as a dominant paradigm in response to the increased need for effective computing services over the internet, data sharing, and resource utilization. Efficient load balancing is crucial in managing resources efficiently for improving the performance, scalability, and reliability of cloud computing environments. This paper presents a comparative performance analysis of three dynamic load balancing algorithms -- Least Loaded, Weighted Round Robin, and Enhanced Load Balancing (ELB) -- for optimizing ERP component allocation in cloud environments. Experiments were conducted using a python-based simulation that dynamically allocates Enterprise Resource Planning (ERP) components to virtualized cloud resources under both uniform and non-uniform configurations. Simulation results reveal that under uniform configuration, ELB achieved the highest CPU utilization (79%) and throughput (295 units) with the lowest response time (39.31%) as compared to traditional algorithms. Similarly, under non-uniform configuration, ELB maintained superior performance with maximum CPU utilization of 78.89%, average utilization of 61.43%, and throughput of 281 units. These results highlight ELB's capability to adapt dynamically to workload variations while efficiently utilizing computational resources and reducing latency. The ELB approach enhances CPU utilization and overall system responsiveness, as a feasible solution for scalable and adaptive ERP-based cloud environments. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. 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
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  Data: A Dynamic Resource-Aware Load Balancing Approach for Optimized Performance in Cloud Computing.
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  Data: <searchLink fieldCode="AR" term="%22Mehta%2C+Sunaina%22">Mehta, Sunaina</searchLink><relatesTo>1</relatesTo><i> sunainabagga@rimt.ac.in</i><br /><searchLink fieldCode="AR" term="%22Bhardwaj%2C+Sushil%22">Bhardwaj, Sushil</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Performability+Engineering%22">International Journal of Performability Engineering</searchLink>. Feb2026, Vol. 22 Issue 2, p110-118. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Cloud+computing%22">Cloud computing</searchLink><br /><searchLink fieldCode="DE" term="%22Load+balancing+%28Computer+networks%29%22">Load balancing (Computer networks)</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+management%22">Resource management</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Scalability%22">Scalability</searchLink><br /><searchLink fieldCode="DE" term="%22Central+processing+units%22">Central processing units</searchLink><br /><searchLink fieldCode="DE" term="%22Enterprise+resource+planning+software%22">Enterprise resource planning software</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink>
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  Label: Abstract
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  Data: Cloud computing has emerged as a dominant paradigm in response to the increased need for effective computing services over the internet, data sharing, and resource utilization. Efficient load balancing is crucial in managing resources efficiently for improving the performance, scalability, and reliability of cloud computing environments. This paper presents a comparative performance analysis of three dynamic load balancing algorithms -- Least Loaded, Weighted Round Robin, and Enhanced Load Balancing (ELB) -- for optimizing ERP component allocation in cloud environments. Experiments were conducted using a python-based simulation that dynamically allocates Enterprise Resource Planning (ERP) components to virtualized cloud resources under both uniform and non-uniform configurations. Simulation results reveal that under uniform configuration, ELB achieved the highest CPU utilization (79%) and throughput (295 units) with the lowest response time (39.31%) as compared to traditional algorithms. Similarly, under non-uniform configuration, ELB maintained superior performance with maximum CPU utilization of 78.89%, average utilization of 61.43%, and throughput of 281 units. These results highlight ELB's capability to adapt dynamically to workload variations while efficiently utilizing computational resources and reducing latency. The ELB approach enhances CPU utilization and overall system responsiveness, as a feasible solution for scalable and adaptive ERP-based cloud environments. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. 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: 10.23940/ijpe.26.02.p6.110118
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      – Code: eng
        Text: English
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      – SubjectFull: Cloud computing
        Type: general
      – SubjectFull: Load balancing (Computer networks)
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      – SubjectFull: Resource management
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      – SubjectFull: Resource allocation
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      – SubjectFull: Scalability
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      – SubjectFull: Central processing units
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      – SubjectFull: Enterprise resource planning software
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      – SubjectFull: Simulation methods & models
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            NameFull: Mehta, Sunaina
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              M: 02
              Text: Feb2026
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              Y: 2026
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