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Mutation-Based Glow Worm Swarm Optimization for Efficient Load Balancing in Cloud Computing
Abstract
Cloud computing has evolved as an innovation that facilitates tasks by dynamically distributing virtual machines. User has to pay for the resources as per the demand. This is a challenging task for cloud service providers. The problems caused in load balancing are selecting random solutions, low speed convergence and picking up the original optima. To attain the best result, a mutation-based glow worm swarm optimization (MGWSO) technique is proposed. With this method, the makespan is reduced for a single work set across multiple datacentres. The work is motivated to decrease the consumption of resources in dynamic contexts while simultaneously increasing their availability. The simulated result shows that the suggested load balancing method dramatically reduces makespan in comparison to mutation-based particle swarm optimization.
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