# Performance Analysis of Selected Machine Learning Techniques for Estimating Resource Requirements of Virtual Network Functions (VNFs) in Software Defined Networks

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## Abstract

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## 1. Introduction

- A comprehensive survey has been carried out for existing ML techniques used for resource management in NFV environment.
- The experiments are carried out using a real traffic trace, to first validate the data set already published by [7].
- Based on experimental testing and related literature, we have modeled multiple linear regression and support-vector-machine-based regression model to optimally estimate the CPU consumption of different VNFs.
- Finally, we compared our adopted models with the artificial-neural-network-based approach proposed by Mestres et al. [7].

#### 1.1. SDN Model

#### 1.2. SDN-with-Knowledge Operation

#### 1.3. Research Objectives

#### 1.4. Resource Management in Network Function Virtualization

#### 1.5. Machine Learning

## 2. Problem Definition and Proposed Models

#### 2.1. Multiple Linear Regression

#### 2.2. Support Vector Machines

#### 2.3. Artificial Neural Networks

#### 2.4. Evaluation Criterion

## 3. Methodology

#### 3.1. Virtual Network Functions VNFs

#### 3.2. Traffic Preprocessing

#### 3.3. Machine Learning Training

## 4. Experimental Results

#### 4.1. Accuracy in Terms of Regression

#### 4.2. Accuracy in Terms of Error Percentage

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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Framework | $\mathbf{Firewall}\text{}\mathbf{Coefficient}\text{}\mathit{R}$ | $\mathbf{OVS}\text{}\mathbf{Coefficient}\text{}\mathit{R}$ | $\mathbf{Snort}\text{}\mathbf{Coefficient}\text{}\mathit{R}$ |
---|---|---|---|

MLR | 0.97259 | 0.94898 | 0.94991 |

SVR | 0.99697 | 0.99629 | 0.99577 |

FNN | 0.80096 | 0.92623 | 0.92819 |

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**MDPI and ACS Style**

Faheem, S.M.; Babar, M.I.; Khalil, R.A.; Saeed, N.
Performance Analysis of Selected Machine Learning Techniques for Estimating Resource Requirements of Virtual Network Functions (VNFs) in Software Defined Networks. *Appl. Sci.* **2022**, *12*, 4576.
https://doi.org/10.3390/app12094576

**AMA Style**

Faheem SM, Babar MI, Khalil RA, Saeed N.
Performance Analysis of Selected Machine Learning Techniques for Estimating Resource Requirements of Virtual Network Functions (VNFs) in Software Defined Networks. *Applied Sciences*. 2022; 12(9):4576.
https://doi.org/10.3390/app12094576

**Chicago/Turabian Style**

Faheem, Sahibzada Muhammad, Mohammad Inayatullah Babar, Ruhul Amin Khalil, and Nagham Saeed.
2022. "Performance Analysis of Selected Machine Learning Techniques for Estimating Resource Requirements of Virtual Network Functions (VNFs) in Software Defined Networks" *Applied Sciences* 12, no. 9: 4576.
https://doi.org/10.3390/app12094576