Virtual machine resource allocation optimization in cloud environments with customized particle swarm optimization
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Author (Corporation)
Publication date
2025
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Collections
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04B - Conference paper
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Parent work
2025 7th International Symposium on Computational and Business Intelligence (ISCBI 2025)
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Pages / Duration
1-7
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Publisher / Publishing institution
IEEE
Place of publication / Event location
Macau
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Abstract
Efficient virtual machine resource allocation is vital in cloud infrastructures to maximize performance and minimize resource overprovisioning. State-of-the-art methods include virtual machine migration, placement, scheduling, and load balancing. This research investigates a customized Particle Swarm Optimization (PSO) algorithm to optimize the performance and resource utilization of virtual machines in the cloud. A cloud simulation environment was developed in Java and Python for conducting experiments using PSO. The results are promising since virtual machines’ resource allocation and performance significantly improved in only a few iterations demonstrating that the proposed PSO is an effective method for optimizing the performance and resource allocation in a cloud environment.
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International Symposium on Computational and Business Intelligence (ISCBI)
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979-8-3315-3378-6
979-8-3315-3377-9
979-8-3315-3377-9
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Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
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peer-reviewed
Open access category
Closed
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Citation
Hoenen, L., Dornberger, R., & Hanne, T. (2025). Virtual machine resource allocation optimization in cloud environments with customized particle swarm optimization. 2025 7th International Symposium on Computational and Business Intelligence (ISCBI 2025), 1–7. https://doi.org/10.1109/ISCBI64586.2025.11015375