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@article{175769,
        author = {T Bharath and A. N. Dinesh Kumar},
        title = {A Smarter Approach to Cloud Computing: Predicting Workloads with AI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4477-4480},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175769},
        abstract = {Cloud computing has revolutionized the IT landscape, providing cost-effective and scalable resources. However, accurate performance prediction for virtual machines (VMs) remains a critical challenge due to their black-box nature and variable workloads. The "Cloud Prophet" framework leverages machine learning techniques, including Dynamic Time Warping (DTW) and neural networks, for precise VM performance prediction. As extensions, Gated Recurrent Unit (GRU) is employed for enhanced accuracy, and live dataset integration demonstrates real-time applicability. The proposed system effectively predicts VM performance degradation and optimizes resource allocation, outperforming existing methods. By addressing key limitations of traditional algorithms, the extended approach provides a robust, scalable solution for managing dynamic cloud environments while achieving high prediction accuracy and operational efficiency.},
        keywords = {Cloud computing, IT landscape, Virtual machines (VMs)},
        month = {April},
        }