This preprint is under consideration at Wireless Personal Communications. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
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Research Article
Latchoumi TP
SRMIST: SRM Institute of Science and Technology
Corresponding Author
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In Cloud Computing (CC), load balancing tasks remain an essential problem of spreading resources from a data center to ensure that each Virtual Machine (VM) has a balanced load to achieve maximum utilization of its capabilities. In the CC world, load balancing is a Non-Polynomial (NP) problem solved with metaheuristic algorithms. A new Quasi Oppositional Dragonfly Algorithm for Load Balancing (QODA-LB) was developed to achieve the optimal resource scheduling in a CC setting. The proposed QODA-LB algorithm uses three variables to compute an objective function: run time, running cost, and load. The QODA-LB algorithm assigns tasks to VM based on its potential and the derivative objective function. Also, the QODA-LB algorithm uses the principle of Quasi-Oppositional Based Learning (QOBL) to increase the standard Dragonfly Algorithm's (DA) convergence rate. A comprehensive series of experiments were conducted, and the findings were analyzed in a variety of ways to ensure the efficient performance increased by the QODA-LB algorithm. The simulation's results demonstrated optimum load balancing efficiency and outperformed the leading approaches.
Keywords
Cloud computing, load scheduling, dragonfly algorithm, oppositional based learning
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CURRENT STATUS: Under Revision
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Posted 30 Mar, 2021
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Editorial decision: Major revisions
On 17 Apr, 2021
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Received 25 Mar, 2021
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On 17 Mar, 2021
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This preprint is under consideration at Wireless Personal Communications. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Latchoumi TP
SRMIST: SRM Institute of Science and Technology
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Revision
Version 1
Posted 30 Mar, 2021
No community comments so far
Editorial decision: Major revisions
On 17 Apr, 2021
Reviews received
Received 25 Mar, 2021
Editor assigned
On 17 Mar, 2021
First submitted
On 14 Mar, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Technical Communication
License:
This work is licensed under a CC BY 4.0 License. Read Full License
In Cloud Computing (CC), load balancing tasks remain an essential problem of spreading resources from a data center to ensure that each Virtual Machine (VM) has a balanced load to achieve maximum utilization of its capabilities. In the CC world, load balancing is a Non-Polynomial (NP) problem solved with metaheuristic algorithms. A new Quasi Oppositional Dragonfly Algorithm for Load Balancing (QODA-LB) was developed to achieve the optimal resource scheduling in a CC setting. The proposed QODA-LB algorithm uses three variables to compute an objective function: run time, running cost, and load. The QODA-LB algorithm assigns tasks to VM based on its potential and the derivative objective function. Also, the QODA-LB algorithm uses the principle of Quasi-Oppositional Based Learning (QOBL) to increase the standard Dragonfly Algorithm's (DA) convergence rate. A comprehensive series of experiments were conducted, and the findings were analyzed in a variety of ways to ensure the efficient performance increased by the QODA-LB algorithm. The simulation's results demonstrated optimum load balancing efficiency and outperformed the leading approaches.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
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