Journal Article

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

Alzubi, Emran, Makhadmeh, Sharif, Al-E’mari, Salam, Fraihat, Salam, Abualhaj, Mosleh, Sanjalawe, Yousef

2025

Overview

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.

• Publisher: Public Library of Science,PLOS,Public Library of Science (PLoS)
• Subject: Algorithms / Artificial intelligence / Artificial neural networks / Biology and Life Sciences / Cloud Computing / Computer and Information Sciences / 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 / Physical Sciences / Reinforcement / Research and Analysis Methods / Resource allocation / Resource management / Resource utilization / Scheduling / Simulation / Social Sciences / Software services / Systems stability / Task scheduling / Workload / Workloads