An Efficient Predictive Resource Analysis Using Deep Dyna Q-Based VARMA LSTM Model for Fluctuating Cloud Workloads

In recent years, cloud computing has experienced rapid growth, which has increased the need for efficient resource management and workload analysis. Existing models for predictive resource analysis during fluctuating cloud workloads lack precision, recall, and accuracy. To address these limitations, we propose a novel Deep Dyna Q-based VARMA LSTM model for predictive cloud resource analysis during workload fluctuations. Deep Dyna Q provides an effective method for predicting future states, VARMA provides a robust method for modeling time series data, and LSTM enables the model to handle sequential data effectively for real-time scenarios. The proposed model is intended to analyze cloud workloads with high accuracy, precision, recall, and low delay. This model's use case is to assist cloud service providers with resource allocation and management by predicting future resource needs using historical datasets and samples. In addition, the model is evaluated for multiple cloud scenarios to ensure its viability in a variety of environments. In our experiments, the proposed model for analyzing cloud workloads achieved 98.5% accuracy, 97.4% precision, 97.9% recall, and 98.9 efficiency of balancing the tasks. In conclusion, the proposed model is a promising approach for predictive cloud workload resource analysis. Deep Dynamic Q, VARMA, and LSTM provide a potent solution for modeling and analyzing cloud workloads, with the potential to significantly enhance resource allocation and management in cloud computing environments.