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With the expansion and enhancement of cloud data centers in recent years, increasing the energy consumptionand the costs of the users have become the major concerns in the cloud research area. Service quality parametersshould be guaranteed to meet the demands of the users of the cloud, to support cloud service providers,and to reduce the energy consumption of the data centers. Therefore, the data center's resources must be managedefficiently to improve energy utilization. Using the virtual machine (VM) consolidation technique is animportant approach to enhance energy utilization in cloud computing. Since users generally do not use all thepower of a VM, the VM consolidation technique on the physical server improves the energy consumption andresource efficiency of the physical server, and thus improves the quality of service (QoS). In this article, a serverthreshold prediction method is proposed that focuses on the server overload and server underload detectionto improve server utilization and to reduce the number of VM migrations, which consequently improves theVM's QoS. Since the VM integration problem is very complex, the exponential smoothing technique is utilizedfor predicting server utilization. The results of the experiments show that the proposed method goes beyondexisting methods in terms of power efficiency and the number of VM migrations.

The authors very much regret that errors have slipped into their contribution.The corrections are listed below. Sahar Adabi's affiliation was incorrect. The correct affiliation is given here. Virtual machine selectionThe subsection should read:There are more than one virtual machines run on a server; thus, it needs to consider how to choose a migratable virtual machine when a server is overloaded. Different parameters involve to select the virtual machine, and at the moment, there are four types of virtual machine selection policies: maximum correlation (MC), minimumThe online version of the original article can be found under

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