  Cloud computing service models have experienced rapid growth and inefficient
resource usage is known as one of the greatest causes of high energy
consumption in cloud data centers. Resource allocation in cloud data centers
aiming to reduce energy consumption has been conducted using live migration of
Virtual Machines (VMs) and their consolidation into the small number of
Physical Machines (PMs). However, the selection of the appropriate VM for
migration is an important challenge. To solve this issue, VMs can be classified
according to the pattern of user requests into sensitive or insensitive classes
to latency, and thereafter suitable VMs can be selected for migration. In this
paper, the combination of Convolution Neural Network (CNN) and Gated Recurrent
Unit (GRU) is utilized for the classification of VMs in the Microsoft Azure
dataset. Due to the fact the majority of VMs in this dataset are labeled as
insensitive to latency, migration of more VMs in this group not only reduces
energy consumption but also decreases the violation of Service Level Agreements
(SLA). Based on the empirical results, the proposed model obtained an accuracy
of 95.18which clearly demonstrates the superiority of our proposed model
compared to other existing models.
