An energy aware resource allocation based on combination of CNN and GRU for virtual machine selection

• Published in: Multimedia tools and applications Vol. 83; no. 9; pp. 25769 - 25796
• Main Authors: Khodaverdian, Zeinab, Sadr, Hossein, Edalatpanah, Seyed Ahmad, Nazari, Mojdeh
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2024; Springer Nature B.V
• Subjects: Artificial neural networks; Classification; Cloud computing; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Datasets; Delay; Empirical analysis; Energy consumption; Energy costs; Model accuracy; Multimedia Information Systems; Predictions; Resource allocation; Special Purpose and Application-Based Systems; Virtual environments; Workload; Workloads; Convolution Neural Network (CNN); Virtual machine selection; Gated Recurrent Unit (GRU); Cloud data centers; Resource allocation
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-16488-2

The use of cloud computing service models is rapidly increasing, but inefficient resource usage in cloud data centers can lead to great energy consumption and costs. To address this issue, plans have been made to allocate resources more efficiently by utilizing live migration of virtual machines (VMs) and consolidating them into a smaller number of physical machines (PMs). Although, selecting a suitable VM for migration is still a significant challenge. One solution is to classify VMs into Delay-Sensitive (Interactive) or Delay-Insensitive classes based on user request patterns, and then select suitable VMs for migration. This selection process can be enabled through workload prediction, which involves predicting and analyzing the workload of VMs as a pre-migration process. A hybrid model based on Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) combination is introduced in this paper for classifying VMs in the Microsoft Azure cloud service. Microsoft Azure Dataset is a labeled dataset and virtual machine workload in this dataset is categorized as Delay-Insensitive or Delay-sensitive (Interactive). However, the samples distribution in this dataset is unbalanced and majority samples are classified as Delay-Insensitive. To address the challenge, Synthetic Minority Oversampling Technique (SMOTE) method is leveraged in this paper. The empirical results showed that their proposed model achieved an accuracy of 95.18%, indicating that it outperformed other existing models.