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Journal of Network and Computer Applications, 166, p.102716ZeinabKhodaverdianHosseinSadrMojdehNazari SoleimandarabiSeyed AhmadEdalatpanah95202316618910.66224/jict.43909.15.55.166http://jour.aicti.ir/en/Article/43909http://jour.aicti.ir/en/Article/Download/43909http://jour.aicti.ir/en/Article/Download/43909http://jour.aicti.ir/en/Article/Download/43909http://jour.aicti.ir/en/Article/Download/43909http://jour.aicti.ir/en/Article/Download/43909http://jour.aicti.ir/en/Article/Download/43909http://jour.aicti.ir/en/Article/Download/43909Cloud computing service models are growing rapidly, and inefficient use of resources in cloud data centers leads to high energy consumption and increased costs. Plans of resource allocation aiming to reduce energy consumption in cloud data centers 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 Delay-sensitive (Interactive) or Delay-Insensitive classes, and thereafter suitable VMs can be selected for migration. This is possible by virtual machine workload prediction .In fact, workload predicting and predicting analysis is a pre-migration process of a virtual machine. In this paper, In order to classification of VMs in the Microsoft Azure cloud service, a hybrid model based on Convolution Neural Network (CNN) and Gated Recurrent Unit (GRU) is proposed. Microsoft Azure Dataset is a labeled dataset and the workload of virtual machines in this dataset are in two labeled Delay-sensitive (Interactive) or Delay-Insensitive. But the distribution of samples in this dataset is unbalanced. In fact, many samples are in the Delay-Insensitive class. Therefore, Random Over-Sampling (ROS) method is used in this paper to overcome this challenge. Based on the empirical results, the proposed model obtained an accuracy of 94.42 which clearly demonstrates the superiority of our proposed model compared to other existing models.منابع و مأخذ[1] A. Yousafzai et al., "Cloud resource allocation schemes: review, taxonomy, and opportunities," Knowledge and Information Systems, vol. 50, no. 2, pp. 347-381, 2017.[2] I. Hamzaoui, B. Duthil, V. Courboulay, and H. Medromi, "A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management," SN Computer Science, vol. 1, no. 2, pp. 1-28, 2020.[3] A. Beloglazov, "Energy-efficient management of virtual machines in data centers for cloud computing," 2013.[4] S. Singh and I. Chana, "A survey on resource scheduling in cloud computing: Issues and challenges," Journal of grid computing, vol. 14, no. 2, pp. 217-264, 2016.[5] M. H. Sayadnavard, A. T. Haghighat, and A. M. 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