Abstract

Load balancing is vital in cloud computing for efficiently distributing workloads and preventing resource bottlenecks. This review explores using Genetic Algorithms (GAs), known for their optimization strength, to improve load balancing by minimizing task execution times and boosting resource utilization. The GA-based approach adapts to changing tasks and conditions, evolving solutions through iterative natural selection processes. This paper examines the key principles and real-world applications of this method, showing its potential to transform traditional load balancing and enhance cloud system performance and scalability.