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

arXiv: https://arxiv.org/abs/2106.12178 · HTML: https://mlsanity.com/paper/2106.12178/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 63 references that are in the map.
- First posted on arXiv 23 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Dive into Deep Learning](https://mlsanity.com/paper/2106.11342/) (2021)
- [A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1510.03820/) (2015)
- [Machine Learning (ML)-Centric Resource Management in Cloud Computing: A Review and Future Directions](https://mlsanity.com/paper/2105.05079/) (2021)
- [Machine learning for cloud resources management -- An overview](https://mlsanity.com/paper/2101.11984/) (2021)
- [A Novel Deep Learning Method for Textual Sentiment Analysis](https://mlsanity.com/paper/2102.11651/) (2021)

## Built on by (most cited first, 1 of 1)

- [Optimized Cloud Resource Allocation Using Genetic Algorithms for Energy Efficiency and QoS Assurance](https://mlsanity.com/paper/2504.17675/) (2025)

## Abstract (arXiv)

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.