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

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Názov: Smart load balancing in cloud computing: Integrating feature selection with advanced deep learning models.
Autori: Sanjalawe, Yousef, Fraihat, Salam, Al-E'mari, Salam, Abualhaj, Mosleh, Makhadmeh, Sharif, Alzubi, Emran
Zdroj: PLoS ONE; 9/9/2025, Vol. 20 Issue 9, p1-50, 50p
Predmety: CLOUD computing, LOAD balancing (Computer networks), DEEP learning, REINFORCEMENT learning, FEATURE selection, RESOURCE management, MATHEMATICAL optimization
Abstrakt: 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 feature selection and deep learning models for optimizing resource allocation. The proposed Smart Load Adaptive Distribution with Reinforcement and Optimization approach, SLADRO, combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) algorithms for load prediction, a hybrid bio-inspired optimization technique—Orthogonal Arrays and Particle Swarm Optimization (OOA-PSO)—for feature selection algorithms, and Deep Reinforcement Learning (DRL) for dynamic task scheduling. Extensive simulations conducted on a real-world dataset called Google Cluster Trace dataset reveal that the SLADRO model significantly outperforms traditional load-balancing approaches, yielding notable improvements in throughput, makespan, resource utilization, and energy efficiency. This integration of advanced techniques offers a scalable and adaptive solution, providing a comprehensive framework for efficient load balancing in cloud computing environments. [ABSTRACT FROM AUTHOR]
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Abstrakt: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 feature selection and deep learning models for optimizing resource allocation. The proposed Smart Load Adaptive Distribution with Reinforcement and Optimization approach, SLADRO, combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) algorithms for load prediction, a hybrid bio-inspired optimization technique—Orthogonal Arrays and Particle Swarm Optimization (OOA-PSO)—for feature selection algorithms, and Deep Reinforcement Learning (DRL) for dynamic task scheduling. Extensive simulations conducted on a real-world dataset called Google Cluster Trace dataset reveal that the SLADRO model significantly outperforms traditional load-balancing approaches, yielding notable improvements in throughput, makespan, resource utilization, and energy efficiency. This integration of advanced techniques offers a scalable and adaptive solution, providing a comprehensive framework for efficient load balancing in cloud computing environments. [ABSTRACT FROM AUTHOR]
ISSN:19326203
DOI:10.1371/journal.pone.0329765