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

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...

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Vydané v:PloS one Ročník 20; číslo 9; s. e0329765
Hlavní autori: Sanjalawe, Yousef, Fraihat, Salam, Al-E’mari, Salam, Abualhaj, Mosleh, Makhadmeh, Sharif, Alzubi, Emran
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 09.09.2025
Public Library of Science (PLoS)
Predmet:
Algorithms
Artificial intelligence
Artificial neural networks
Cloud Computing
Datasets
Deep Learning
Energy consumption
Energy efficiency
Feature selection
Humans
Innovations
Internet access
Literature reviews
Load balancing
Load balancing (Computers)
Load distribution
Long short-term memory
Machine learning
Methods
Neural networks
Neural Networks, Computer
Optimization algorithms
Orthogonal arrays
Particle swarm optimization
Reinforcement
Resource allocation
Resource management
Resource utilization
Scheduling
Simulation
Software services
Systems stability
Task scheduling
Workload
Workloads
Jordan
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí: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.
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ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0329765