All over the world, cloud computing offers a shared resources pool for several users. The cloud model encompasses the virtual machines and PMs from the users in order to processes the tasks in an equivalent way. In particular circumstances, the demand for users might be maximum that causes the processing units overloading and this circumstance influences the evaluation of the cloud setup. To balance the load of the cloud environment, numerous works have been proposed for the load balancing approach; however, they need to decrease the quantity of task migration. This model develops the balancing of load approach adopting crucial the optimization technique and the multi-objective design. This work develops chicken swarm and enhanced raven roosting optimization, for balancing the load (CS-ERRO) algorithm via the hybridization of the Chicken Swarm Algorithm (CSA), Enhanced Raven Roosting Optimization Algorithm (ERROA). Additionally, this work exploits the multi-objective model on the basis of the probabilities selection, the frequency scaling due to the ability of the machine and the data length of the task. Finally, the performance of the proposed CS-ERRO algorithm is evaluated in different cloud cases, and from the consequences, it is obvious that the proposed algorithm attained reduced load while comparing with the conventional algorithms.
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