Cloud computing is an integrated service, which provides a new versatile of programming paradigm with a good number of features to dynamically migrate virtualized computing services between physical servers of various cloud data centers. This technology made the users and cloud service providers (CSPs) to manage the loads as per the demand. The effective management of loads in the cloud environment gives a rise to introduce new management methods, which are responsible for equally distributing the incoming workloads among all the available VMs in an effective way for a given period of time. To achieve this requirement various schemes have been proposed. However, many of them are not suitable to find the optimal information about required resources to fulfil the user demands. To accommodate this, in this paper, an approach named heuristic and fair-queuing based VM load balancing (HFQ-LB) has been introduced. With the help of fair-queuing, an efficient strategy has been designed. This configures the number of incoming loads for finding an appropriate VM for the assignment to satisfy the goal, i.e., it maximizes the utilization of resources according to the user demand. With the help of CloudSim, the proposed algorithm is validated in terms of makespan, waiting time, CPU utilization, and VM utilization by creating a multiple number of data centers. The proposed algorithm is also compared with other existing eight algorithms. Experimental results show that the proposed algorithm outperforms these existing algorithms in terms of makespan, waiting time, CPU utilization, and VM utilization.
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