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Review Article Open Access June 28, 2016

Scalable Task Scheduling in Cloud Computing Environments Using Swarm Intelligence-Based Optimization Algorithms

Srinivas Chippagiri 1,*, Savan Kumar 1 and Sumit Kumar 1
1
University of Utah, Salt Lake City, Utah, USA
Publihed in: Current Research in Public Health (Volume 1, Issue 1, 2021)
Page(s): 1-10
DOI: 10.31586/jaibd.2016.1291
Received
March 06, 2016
Revised
April 19, 2016
Accepted
May 11, 2016
Published
June 28, 2016
Keywords
Cloud Computing; Task Scheduling; Swarm Intelligence; Particle Swarm Optimization (PSO); Artificial Bee Colony (ABC)
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright: Copyright © The Author(s), 2021. Published by Scientific Publications
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Cite This Article

APA Style
Chippagiri, S. , Kumar, S. , & Kumar, S. (2021). Scalable Task Scheduling in Cloud Computing Environments Using Swarm Intelligence-Based Optimization Algorithms. Current Research in Public Health, 1(1), 1-10. https://doi.org/10.31586/jaibd.2016.1291
ACS Style
Chippagiri, S. ; Kumar, S. ; Kumar, S. Scalable Task Scheduling in Cloud Computing Environments Using Swarm Intelligence-Based Optimization Algorithms. Current Research in Public Health 2021 1(1), 1-10. https://doi.org/10.31586/jaibd.2016.1291
Chicago/Turabian Style
Chippagiri, Srinivas, Savan Kumar, and Sumit Kumar. 2021. "Scalable Task Scheduling in Cloud Computing Environments Using Swarm Intelligence-Based Optimization Algorithms". Current Research in Public Health 1, no. 1: 1-10. https://doi.org/10.31586/jaibd.2016.1291
AMA Style
Chippagiri S, Kumar S, Kumar S. Scalable Task Scheduling in Cloud Computing Environments Using Swarm Intelligence-Based Optimization Algorithms. Current Research in Public Health. 2021; 1(1):1-10. https://doi.org/10.31586/jaibd.2016.1291 
@Article{crph1291,
AUTHOR = {Chippagiri, Srinivas and Kumar, Savan and Kumar, Sumit},
TITLE = {Scalable Task Scheduling in Cloud Computing Environments Using Swarm Intelligence-Based Optimization Algorithms},
JOURNAL = {Current Research in Public Health},
VOLUME = {1},
YEAR = {2021},
NUMBER = {1},
PAGES = {1-10},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/1291},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2016.1291},
ABSTRACT = {Effective task scheduling in cloud computing is crucial for optimizing system performance and resource utilization. Traditional scheduling methods often struggle to adapt to the dynamic and complex nature of cloud environments, where workloads, resource availability, and task requirements constantly change. Swarm intelligence-based optimization algorithms, such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC), offer a promising solution by mimicking natural processes to explore large search spaces efficiently. These algorithms are effective in balancing multiple objectives, including minimizing execution time, reducing energy consumption, and ensuring fairness in resource allocation. They also enhance system scalability, which is vital for modern cloud infrastructures. However, challenges remain, including slow convergence speeds, complex parameter tuning, and integration with existing cloud frameworks. Addressing these issues will be essential for the practical implementation of swarm intelligence in cloud task scheduling, helping to improve resource management and overall system performance.},
}
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AB  - Effective task scheduling in cloud computing is crucial for optimizing system performance and resource utilization. Traditional scheduling methods often struggle to adapt to the dynamic and complex nature of cloud environments, where workloads, resource availability, and task requirements constantly change. Swarm intelligence-based optimization algorithms, such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC), offer a promising solution by mimicking natural processes to explore large search spaces efficiently. These algorithms are effective in balancing multiple objectives, including minimizing execution time, reducing energy consumption, and ensuring fairness in resource allocation. They also enhance system scalability, which is vital for modern cloud infrastructures. However, challenges remain, including slow convergence speeds, complex parameter tuning, and integration with existing cloud frameworks. Addressing these issues will be essential for the practical implementation of swarm intelligence in cloud task scheduling, helping to improve resource management and overall system performance.
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