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Abstract

The Bidirectional Long Short-Term Memory (Bi-LSTM) network structure enables data analysis, enhances decision-making processes, and optimizes resource allocation in cloud computing systems. However, achieving peak network performance relies heavily on choosing the hyperparameters for configuring the network. Enhancing resource allocation improves the Service Level Agreement (SLA) by ensuring efficient utilization and allocation of computational resources based on dynamic workload demands. This paper proposes an approach that integrates a Multi-Objective Evolutionary Algorithm (MOEA) with deep learning techniques to address this challenge. This approach combines the optimization capabilities of MOEA with the learning predictive models to establish a framework for resource allocation in cloud environments. Snake Optimizer Algorithm (SOA) and Genetic Algorithm (GA) are utilized to identify specific hyperparameters through optimization procedures, which serve as the foundation for setting up the Long Short-Term Memory (LSTM) network. The search process begins with a random selection of parameters. The process is iterative in nature and concentrates on the best results for the parameters, with initial random sampling done according to the results obtained. This methodology considers all areas and examines the most effective areas, which improves the effectiveness of the approach when solving the problem of moving through the hyperparameter space. The modifications that were made to the snake algorithm have the goal of optimizing resource management within the data center in cloud environment. LSTM-SOA approach achieved an accuracy level of 97% on the dataset created in the Cloudsim simulation environment. This approach is based on multi-objectives to meet the requirements of determining the optimal data center. By comparison, the LSTM-AG approach achieved an accuracy level of 87%.

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