A Machine Learning Framework for Predictive Workload Modeling and Dynamic Cloud Resource Allocation
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I1P107Keywords:
Cloud computing, workload prediction, resource optimization, machine learning, deep learning, LSTM, Transformer models, federated learningAbstract
Cloud computing has fundamentally transformed information technology infrastructure by providing scalable, on-demand resources, yet unpredictable workload variations continue to challenge efficient resource allocation, often resulting in increased operational costs or performance degradation. This paper presents a comprehensive AI-driven workload prediction framework that leverages advanced machine learning architectures, specifically Long Short-Term Memory (LSTM) networks and Transformer models, to anticipate workload fluctuations and optimize cloud resource allocation proactively. The proposed framework is designed to maximize resource utilization efficiency, minimize operational expenses, and enhance service reliability in dynamic cloud environments. Through rigorous experimental evaluation, the AI-based prediction models demonstrate superior performance compared to traditional heuristic approaches, achieving significant improvements in both prediction accuracy and resource optimization metrics. The study concludes by identifying promising future research directions, including the integration of reinforcement learning for adaptive system behavior and federated learning techniques for privacypreserving, collaborative model training across distributed environments, thereby advancing toward more intelligent and resilient cloud infrastructure management
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