Cloud computing(CC), which utilizes massively virtualized data centers to deliver quick and affordable computing solutions, has developed into an established industrial standard that is growing quickly. To handle such a massive amount of data effectively, cloud computing mostly relies on automation and dynamic resource management. In cloud computing, load balancing (LB) is a vital technique for maximizing resource utilization and making sure that no resource is used up. Without the requirement for physical infrastructure, cloud LB allows online platforms to adjust their resources in response to traffic demands. In a cloud environment, workload and resource allocation entail determining the best way to divide up work among several servers. For increasingly severe uncertainty problems, traditional LB approaches are simple but ineffective; for this reason, meta-heuristic methods are employed. This algorithm is heuristic and is independent of the complexity of the challenges. Meta-heuristics approaches based on Artificial Intelligence (AI) are employed to analyze real-time data and intelligently distribute workload among servers. This ensures efficient operations by preventing bottlenecks and enabling proactive LB decisions. The review offers a thorough analysis of meta-heuristics techniques based on artificial intelligence (AI) for static and dynamic LB in both homogeneous and heterogeneous cloud systems.
Balaji et al. (Thu,) studied this question.
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