Abstract: Load balancing in cloud computing environments is a critical area of research that ensures efficient resource utilization, minimized response times, and improved overall system performance. This survey paper provides an extensive review of various load balancing strategies and algorithms employed in cloud computing, categorizing techniques into heuristic, meta-heuristic, hybrid, and machine learning-based approaches. The problem involves distributing dynamic workloads across diverse computing resources to prevent bottlenecks and ensure efficient processing. Numerous algorithms have been developed to address this issue, each with specific strengths and weaknesses. Key studies, including recent advancements and emerging trends, are highlighted to offer a comprehensive understanding of the state-of-the-art in load balancing for cloud computing. The results demonstrate the effectiveness of various algorithms in enhancing cloud performance, with reinforcement learning-based approaches and hybrid algorithms showing particular promise. This survey underscores the importance of developing advanced techniques to address evolving challenges, with future research directions focusing on integrating AI and machine learning for more adaptive solutions.
Keywords: Load balancing, Cloud computing, Heuristic approaches, Machine learning, Resource utilization
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DOI:
10.17148/IJARCCE.2024.13718
