AI-Enabled Workload Prediction for Elastic Cloud Computing Systems

  • Authors

    • Dr. John Peterson Professor, Stanford University, USA. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2024PII0H9K

    Published 12-05-2024

  • Cloud Computing, Workload Prediction, Artificial Intelligence, Elastic Resource Provisioning, Machine Learning, Deep Learning, Lstm, Resource Management, Sla Optimization, Predictive Analytics

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

    How to Cite

    [1]
    J. Peterson, “AI-Enabled Workload Prediction for Elastic Cloud Computing Systems”, IJADSMC, vol. 7, no. 2, pp. 01–16, Dec. 2024, doi: 10.67228/30713498/IJADSMC-2024PII0H9K.

  • Abstract

    In conclusion, the proposed AI-based workload prediction framework significantly enhances cloud resource management by accurately forecasting future workload demands and enabling proactive resource allocation. By utilizing machine learning and deep learning techniques such as LSTM, Random Forest Regression, and Gradient Boosting, the system improves resource utilization, reduces response time, lowers operational costs, and minimizes SLA violations. The results demonstrate superior prediction accuracy compared to traditional methods, leading to better Quality of Service (QoS) and energy efficiency. This study highlights the potential of AI-driven predictive analytics to transform cloud computing from reactive resource management to intelligent, autonomous, and adaptive cloud ecosystems. Future research can further improve performance through the integration of federated learning, reinforcement learning, and edge-cloud computing technologies.

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