Investigating the Significance of Optimization Techniques in Load Balancing in a Cloud Environment

Load balancing is essential in cloud computing, facilitating efficient resource utilization, improved performance, and decreased latency in dynamic and dispersed settings. Over the years, many optimization strategies, heuristic, metaheuristic, and hybrid, have been developed to handle load balancing issues. This study offers a thorough summary of earlier work on the topic, emphasizing the applied techniques and methodologies. Standard methods, including PSO, ACO, and Dragonfly Optimization, are examined and contrasted with HBATAABC and HHO-ACO. Comparative examination reveals the advantages and drawbacks of certain approaches in terms of flexibility, scalability, computing complexity, energy economy, and compatibility for several cloud settings. Among the paper's noted research flaws include inadequate real-time flexibility, integration with developing technologies, and the absence of comprehensive security-aware policies. The objective of this work is to provide a comprehensive study of the evolution of load balancing methods along with recommendations for next solutions able to satisfy the needs of contemporary cloud systems.