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Cloud computing service models have experienced rapid growth and inefficientresource usage is known as one of the greatest causes of high energyconsumption in cloud data centers. Resource allocation in cloud data centersaiming to reduce energy consumption has been conducted using live migration ofVirtual Machines (VMs) and their consolidation into the small number ofPhysical Machines (PMs). However, the selection of the appropriate VM formigration is an important challenge. To solve this issue, VMs can be classifiedaccording to the pattern of user requests into sensitive or insensitive classesto latency, and thereafter suitable VMs can be selected for migration. In thispaper, the combination of Convolution Neural Network (CNN) and Gated RecurrentUnit (GRU) is utilized for the classification of VMs in the Microsoft Azuredataset. Due to the fact the majority of VMs in this dataset are labeled asinsensitive to latency, migration of more VMs in this group not only reducesenergy consumption but also decreases the violation of Service Level Agreements(SLA). Based on the empirical results, the proposed model obtained an accuracyof 95.18which clearly demonstrates the superiority of our proposed modelcompared to other existing models.

Combination of Convolutional Neural Network and Gated Recurrent Unit for Energy Aware Resource Allocation