Optimization of Load Balancing in Cloud Computing through Nature-Inspired Metaheuristic Algorithms

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Roopali Gupta, Om Prakash Sharma

Abstract

Cloud computing has introduced completely new ways to administer resources and distribute services, thereby enhancing load balancing for better service provision. Based on this assumption, this paper presents an approach to optimize load balancing in cloud environments through Nature-Inspirited Metaheuristic Algorithms. These algorithms tend to imitate natural processes such as evolution, swarm behavior, and biological adaptation. Therefore, NIMA can be strong solutions in addressing the complexities of multi-criteria service selection inside dynamic cloud environments by finding workloads efficiently, enhancing system efficiency, minimizing latency, and ensuring better resource utilization. The approach presented in this paper evaluates these nature-inspired algorithms towards possible comparison of their efficiency in handling the diverse and changing demands for cloud services, considering both Particle Swarm Optimization and Genetic Algorithms. The outcome justifies the effectiveness of load balancing, offering a scalable and flexible solution to service providers of cloud services. The outcome presented in the paper further suggest to speed up the cloud computing process by optimizing the distribution of loads and improving the quality of services delivered.


 

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