A fuzzy-based method for cloud service migration using a shark smell optimization algorithm

Computation should develop and become more powerful and flexible as a result of the expansion of apps and the incorporation of novel consumers into the realm of computing systems. The potential of live virtual machine (VM) migration among various clouds is one of the growing study fields in cloud computing. It can be more important when the cloud service quality that a user is presently using deteriorates or when a new cloud service is launched that is superior in performance, quality, and pricing to the existing services. Because this problem is NP-Hard in nature, this article proposes a method to migrate the load from an over-loaded VMs to the active machine that is least loaded using a fuzzy-based shark smell optimization algorithm. This algorithm is based on a shark's ability to discover prey as a strong hunter in nature, based on the shark's smell sense and motion to the odor source. The suggested optimization technique mathematically models diverse shark behaviors in the search environment, which is seawater. The CloudSim is utilized to illustrate the efficiency of the approach compared to others. The outcomes reveal that energy consumption and execution time are better than Genetic Algorithm and Particle Swarm Optimization algorithms.