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Research on the optimal scheduling strategy of cloud computing resources based on genetic algorithm

Yanan Cui

Yanan Cui

College of Information Science and Engineering, Liuzhou Institute of TechnologyLiuzhou, China
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Cui, Yanan
 y 
Yanhua Hu

Yanhua Hu

College of Information Science and Engineering, Liuzhou Institute of TechnologyLiuzhou, China
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Hu, Yanhua
  
26 mar 2025
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Publicado en línea: 26 mar 2025
Recibido: 09 nov 2024
Aceptado: 26 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0815
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© 2025 Yanan Cui et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

VM deployment results
VM deployment results

Figure 2.

Crossover operator execution process
Crossover operator execution process

Figure 3.

Flowchart of improved genetic algorithm
Flowchart of improved genetic algorithm

Figure 4.

The algorithm is compared with the number of algorithms
The algorithm is compared with the number of algorithms

Figure 5.

The convergence of the five algorithms is the result
The convergence of the five algorithms is the result

Figure 6.

Five algorithms load equilibrium comparison results
Five algorithms load equilibrium comparison results

Figure 7.

The comparison results of energy consumption and CPU,memory idle rate
The comparison results of energy consumption and CPU,memory idle rate

Figure 8.

The total utility value of different algorithms is compared
The total utility value of different algorithms is compared
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Ciencias de la vida, Ciencias de la vida, otros, Matemáticas, Matemáticas aplicadas, Matemáticas generales, Física, Física, otros
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