Abstract
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.
Highlights
Cloud computing has been raised as one of the most crucial exciting technological developments, which is growing rapidly to provide almost unlimited process, storage, and network resources for its users [13]
The growing interest in cloud resources has led to the creation of enormous cloud data centers, which need a lot of energy, and cause a large amount of operational costs
Worldwide data center power utilization information shows a nonlinear increase over the past ten years and a similar trend is predicted for the future [28]
Summary
Cloud computing has been raised as one of the most crucial exciting technological developments, which is growing rapidly to provide almost unlimited process, storage, and network resources for its users [13]. The growing interest in cloud resources has led to the creation of enormous cloud data centers, which need a lot of energy, and cause a large amount of operational costs. Cloud providers utilize a lot of data centers around the world [27]. Energy is one of the significant factors of the entire cost of managing a data center and its facilities [18]. Several methods have been proposed in the literature to enhance resource utilization in cloud environments to lead to energy efficiency. Multiple RL-based agents were utilized in [7] for online scheduling of dependent tasks of the cloud's workflows to improve resource utilization. Cooperative RL agents managed the cloud resources in [8] for multiple online scientific workflows. SARSA RL agents and genetic algorithm were used in [9] for cloud resource utilization
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