Smart load balancing in cloud computing: Integrating feature selection with advanced deep learning models.

ABSTRACT: The increasing dependence on cloud computing as a cornerstone of modern technological infrastructures has introduced significant challenges in resource management. Traditional load-balancing techniques often prove inadequate in addressing cloud environments' dynamic and complex nature, resulting in suboptimal resource utilization and heightened operational costs. This paper presents a novel smart load-balancing strategy incorporating advanced techniques to mitigate these limitations. Specifically, it addresses the critical need for a more adaptive and efficient approach to workload management in cloud environments, where conventional methods fall short in handling dynamic and fluctuating workloads. To bridge this gap, the paper proposes a hybrid load-balancing methodology that integrates f

SUBMITTER: Sanjalawe Y 

PROVIDER: S-EPMC12419607 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature