Energy-Aware and Proactive Host Load Detection in Virtual Machine Consolidation

Abstract

With the expansion and enhancement of cloud data centers in recent years, increasing the energy consumption
and the costs of the users have become the major concerns in the cloud research area. Service quality parameters
should 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 managed
efficiently to improve energy utilization. Using the virtual machine (VM) consolidation technique is an
important approach to enhance energy utilization in cloud computing. Since users generally do not use all the
power of a VM, the VM consolidation technique on the physical server improves the energy consumption and
resource efficiency of the physical server, and thus improves the quality of service (QoS). In this article, a server
threshold prediction method is proposed that focuses on the server overload and server underload detection
to improve server utilization and to reduce the number of VM migrations, which consequently improves the
VM's QoS. Since the VM integration problem is very complex, the exponential smoothing technique is utilized
for predicting server utilization. The results of the experiments show that the proposed method goes beyond
existing methods in terms of power efficiency and the number of VM migrations.