Nature-Inspired Metaheuristic Algorithms for Optimization in Cloud Computing: A Review and Analysis

This review and analysis paper explores the application of nature-inspired metaheuristic algorithms for optimization in cloud computing. Cloud computing has become a popular technology for providing on-demand computing resources, but efficient resource allocation and management in cloud environments remain challenging due to their dynamic and complex nature. Nature-inspired metaheuristic algorithms have shown promise in solving optimization problems in various domains. In this paper, we provide a comprehensive review of the application of nature-inspired metaheuristic algorithms in cloud computing environments. We discuss the advantages and limitations of different algorithms and their suitability for various cloud optimization problems, including resource allocation, task scheduling, load balancing, energy efficiency, and security. Furthermore, we provide a comparative analysis of the performance of various nature-inspired metaheuristic algorithms in solving cloud optimization problems. Finally, we highlight the research gaps and future directions for the application of these algorithms in cloud computing optimization. The findings of this review and analysis paper will be useful for further exploration in optimizing resource allocation and management in cloud computing environments. The performance metric execution time(ms) is used for analysis of the optimization techniques in cloud environment.