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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
 and 
Yanhua Hu

Yanhua Hu

College of Information Science and Engineering, Liuzhou Institute of TechnologyLiuzhou, China
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Hu, Yanhua
  
Mar 26, 2025
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Published Online: Mar 26, 2025
Received: Nov 09, 2024
Accepted: Feb 26, 2025
DOI: https://doi.org/10.2478/amns-2025-0815
Keywords
© 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
Language:
English
Publication timeframe:
1 times per year
Journal Subjects:
Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other
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