A Comparative Analysis of Metaheuristic Techniques for High Availability Systems Article Swipe
Darakhshan Syed
,
Ghulam Muhammad Shaikh
,
Hani Alshahrani
,
Mohammed Hamdi
,
Mohammad Alsulami
,
Asadullah Shaikh
,
Syed Rizwan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3352078
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3352078
In the ever-evolving technological landscape, ensuring high system availability has become a paramount concern. This research paper focuses on cloud computing, a domain witnessing exponential growth and emerging as a critical use case for high-availability systems. To fulfil the criteria, many services in cloud infrastructures should be combined, relying on the user’s demands. Central to this study is load balancing, an integral element in harnessing the full potential of heterogeneous computing systems. In cloud environments, dynamic management of load balancing is crucial. This study explores how virtual machines can effectively remap resources in response to fluctuating loads dynamically, optimizing overall network performance. The core of this research involves an in-depth analysis of several metaheuristic algorithms applied to load balancing in cloud computing. These include Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Artificial Bee Colony, and Grey Wolf Optimization. Utilizing CloudAnalyst, the study conducts a comparative analysis of these techniques, focusing on key performance metrics such as Total Response Time (TRT) and Data Center Processing Time (DCPT). The findings of this research offer insights into the varying behaviors of these algorithms under different cloud configurations and user retention levels. The ultimate aim is to pave the way for developing innovative load-balancing strategies in cloud computing. By providing a comprehensive evaluation of existing metaheuristic methods, this paper contributes to advancing high-availability systems, underscoring the importance of tailored solutions in the dynamic realm of cloud technology.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3352078
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10387686.pdf
- OA Status
- gold
- Cited By
- 8
- References
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390692421
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390692421Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3352078Digital Object Identifier
- Title
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A Comparative Analysis of Metaheuristic Techniques for High Availability SystemsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Darakhshan Syed, Ghulam Muhammad Shaikh, Hani Alshahrani, Mohammed Hamdi, Mohammad Alsulami, Asadullah Shaikh, Syed RizwanList of authors in order
- Landing page
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https://doi.org/10.1109/access.2024.3352078Publisher landing page
- PDF URL
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10387686.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10387686.pdfDirect OA link when available
- Concepts
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Computer science, Cloud computing, Metaheuristic, Load balancing (electrical power), Distributed computing, Particle swarm optimization, Ant colony optimization algorithms, Virtual machine, Artificial intelligence, Algorithm, Operating system, Mathematics, Grid, GeometryTop concepts (fields/topics) attached by OpenAlex
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8Total citation count in OpenAlex
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2025: 4, 2024: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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