Context-Aware AI Models for Dynamic Resource Management in Cloud Systems

  • Authors

    • Dr. Richard Evans Professor, University of Florida, USA. Author
    • Dr. Karen Lewis Associate Professor, Ohio State University, USA. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2025PI6V4Y

    Published 03-04-2025

  • Cloud Computing, Context-Aware Computing, Artificial Intelligence, Dynamic Resource Allocation, Machine Learning, Deep Learning, Reinforcement Learning, Cloud Resource Management, SLA Optimization, Autonomous Cloud Systems

    Issue

    Vol. 8 No. 1 (2025)

    Section

    Articles

    How to Cite

    [1]
    R. Evans and K. Lewis, “Context-Aware AI Models for Dynamic Resource Management in Cloud Systems”, IJADSMC, vol. 8, no. 1, pp. 01–16, Mar. 2025, doi: 10.67228/30713498/IJADSMC-2025PI6V4Y.

  • Abstract

    Cloud computing provides scalable and cost-effective resources for modern digital enterprises, but increasing workload diversity, changing user demands, and complex infrastructures make resource management challenging. Traditional resource allocation methods often fail to adapt to dynamic cloud environments, resulting in inefficient resource usage, SLA violations, and higher operational costs. This study proposes a Context-Aware AI framework for dynamic cloud resource management that incorporates workload patterns, user behavior, network conditions, infrastructure health, and business objectives. The framework combines context acquisition, real-time analytics, Long Short-Term Memory (LSTM) workload prediction, Deep Reinforcement Learning (DRL)-based optimization, and adaptive orchestration.  Experimental results show improved resource utilization, response time, energy efficiency, cost reduction, and service reliability. The framework supports autonomous cloud management and provides a foundation for future technologies such as edge computing, IoT, 6G networks, and intelligent enterprise applications.

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