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AI-driven resource scheduling optimisation model and its system architecture design for digital library management

Yu Zhao

Yu Zhao

Library, Zhongkai University of Agriculture and EngineeringGuangzhou, China
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Zhao, Yu
 et 
Ying Che

Ying Che

Library, Zhongkai University of Agriculture and EngineeringGuangzhou, China
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Che, Ying
  
17 mars 2025
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Publié en ligne: 17 mars 2025
Reçu: 22 oct. 2024
Accepté: 30 janv. 2025
DOI: https://doi.org/10.2478/amns-2025-0156
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© 2025 Yu Zhao et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

ARIMA model construction flowchart
ARIMA model construction flowchart

Figure 2.

Frame diagram of decomposed ARMI-LSTM resource prediction model
Frame diagram of decomposed ARMI-LSTM resource prediction model

Figure 3.

Dynamic resource scheduling process
Dynamic resource scheduling process

Figure 4.

Compare the node resource utilization ratio
Compare the node resource utilization ratio

Figure 5.

Time consumption contrast diagram
Time consumption contrast diagram

Figure 6.

Dynamic small-scale simulation load equalization
Dynamic small-scale simulation load equalization

Figure 7.

Dynamic small-scale simulation of delay changes
Dynamic small-scale simulation of delay changes
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Sciences de la vie, Sciences de la vie, autres, Mathématiques, Mathématiques appliquées, Mathématiques générales, Physique, Physique, autres
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