A Comprehensive Analysis of Load Balancing in Cloud Computing: Examining Methodologies and Research Practices for an Effective Hybrid Approach

Over the last several years, cloud computing (CC) has become a unique paradigm. Cloud computing aims to deliver computing and resources over the internet through the dynamic provision of services. Using cloud computing comes with a variety of challenges and obstacles. This study examines load balancing (LB), one of the primary issues of cloud computing. The goal of load balancing is to evenly distribute the computing power of cloud servers, preventing any host from experiencing overwork or underload. Numerous load-balancing algorithms have been implemented in the literature to provide efficient management, fulfill customer requirements for appropriate cloud nodes, enhance the overall effectiveness of cloud services, and improve end-user satisfaction. An effective load-balancing algorithm distributes the workload among system nodes to maximize efficiency and asset utilization. This research paper aims to critically analyze the latest load-balancing approaches. It will cover various load balancing attributes such as resource utilization, scalability, fault tolerance (FT), power savings, throughput performance, migration time, and reaction time. The study report also discusses load balancing issues in cloud computing environments and emphasizes the necessity for a unique technique that utilizes machine learning criteria for load balancing. It has been found that traditional load-balancing algorithms perform poorly and do not consider reliability. Hence, the research paper identifies the need for reliability in load-balancing algorithms, which is one of the main concerns in cloud environments. A new hybrid method is proposed, which utilizes reliability for load balancing.