Efficient load balancing in cloud computing using HHO improved by differential perturbed velocity and TEO

Abstract

Load balancing is one of the primary aspects of cloud computing to avoid situations of being overloaded or underloaded in the node. The paper aims to carry out the dynamic load balancing of non-determent independent tasks in the cloud network and resolved through the hybridisation of an improved version of the Harris Hawks Optimisation Algorithm (HHO) improved by differential perturbed velocity and Thermal Exchange Optimisation (TEO). The main motivations of hybridising are to intensify the diversification ability of the device through the load balance with the VMs, optimise different matrices and enhance the convergence speed. The strength of the algorithm has been authenticated by relating the outcome gained from simulation and real platform processes with the surviving load balancing. The conclusions drawn from the simulation and comparison results illustrate that the projected procedure is outstripping its opponent in the manner of different matrices.