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

TL;DR

This paper proposes a CNN-GRU based model to classify virtual machines in cloud data centers, enabling energy-efficient resource allocation by migrating insensitive VMs, which reduces energy use and SLA violations.

Contribution

The study introduces a novel CNN-GRU model for VM classification, improving accuracy in identifying migration candidates for energy savings in cloud data centers.

Findings

Achieved 95.18% classification accuracy.

Migrating insensitive VMs reduces energy consumption and SLA violations.

Model outperforms existing classification approaches.

Abstract

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.