Abstract

Load balancing is essential in cloud computing to distribute workloads efficiently, prevent resource bottlenecks, and optimize utilization. This review explores a novel approach using Genetic Algorithms (GAs), which leverage natural evolution principles to enhance task scheduling and minimize execution makespan. The GA-based strategy adapts dynamically to environmental changes, continuously refining solutions for improved performance and scalability. By examining its fundamental principles and real-world applications, this review highlights the potential of GA-driven load balancing to transform traditional cloud computing paradigms.