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January 1, 2026International Journal of Engineering Systems Modelling and Simulation

Deep learning-driven forecasting of cloud resource utilisation using PSO-enhanced LSTM for cost-effective scaling

Karthikeyan Sivanandi C. Sathish Kumar Dr.K. Chitra S.Silvia Priscila G. Rajasekaran S. Suman Rajest

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Key Points

The aim is to develop a deep learning model for accurately forecasting cloud resource utilization.
Utilized a PSO-enhanced LSTM algorithm for forecasting
Evaluated resource utilization data
Implemented cost-effective scaling strategies
Achieved significant accuracy improvements in resource utilization forecasts
Demonstrated effective cost management in cloud scaling
Indicated potential reductions in operational expenses through improved forecasting

Implication

Demonstrates cost-effective scaling of cloud resources using deep learning techniques, indicating improved efficiency.

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Cite This Study

Sivanandi et al. (2026) studied this question.

synapsesocial.com/papers/6a22698b763171746d548264 https://doi.org/10.1504/ijesms.2026.10078937

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

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