A systematic review of various load balancing approaches in cloud computing utilizing machine learning and deep learning

Load balancing in cloud computing plays a vital role in optimizing resource utilization, enhancing performance, and managing task allocation within dynamic and highly virtualized environments. This review paper comprehensively explores the diverse spectrum of load balancing methods employed in cloud computing, shedding light on their characteristics, advantages, and limitations. More advanced load balancing techniques leverage intelligent algorithms and real-time data to make dynamic decisions. Both machine and deep learning-based approaches, including reinforcement learning and neural networks, have gained prominence for their ability to adapt to changing workloads and traffic patterns. These methods exhibit great promise in optimizing resource allocation and improving overall system performance. Additionally, this review delves into emerging trends such as edge computing, hybrid cloud deployments, and container orchestration, exploring the evolution of load balancing strategies to meet the demands of these evolving paradigms. This review paper offers a thorough overview of load balancing techniques in cloud computing, equipping researchers, practitioners, and cloud architects with essential insights for choosing the most appropriate load balancing strategies tailored to their specific needs and use cases. It also highlights key challenges and outlines future research directions in this evolving field.