Post-migration VM model with Task Scheduling using Swarm Optimization for Load Balancing

Abstract

Cloud computing has evolved as a methodology that grazes operations by automatically assigning virtual machines. Consumers compensate for programmes, based on their demand. There are also problems with a cloud service. One of the key challenges is the post-migration VM load balancing model, which suffers from many problems, including premature convergence, reduced convergence schedules, random solutions at first, and a native optimal solution. The proposed method considered the Post-migration VM Task Plan Model utilising Swarm Optimization for Load Balancing to resolve the heuristic approach problem that has already been discussed. The suggested solution focuses on the Particle Swarm algorithm for mutation-based load balancing between data centres. In this case, an efficient load balancing algorithm is built, which reduces performance parameters such as post migration VM model time and improves cloud health.