Estimating VNF resource requirements using machine learning techniques

Houda Jmila; Mohamed Ibn Khedher; Mounim A. El Yacoubi

doi:10.1007/978-3-319-70087-8_90

Estimating VNF resource requirements using machine learning techniques

Houda Jmila
, Mohamed Ibn Khedher
, Mounim A. El Yacoubi

Université Paris-Saclay

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Resource Management in the network function virtualization (NFV) environment is a challenging task. The continuously varying demands of virtual network functions (VNF) call for dynamic algorithms to efficiently scale the allocated resources and meet fluctuating needs. In this context, studying the behavior of a VNF as a function of its environment helps to model its resource requirements and thus allocate them dynamically. This paper investigates the use of machine learning techniques to estimate VNFs needs in term of CPU as a function of the traffic they will process. We propose and adapt a Support Vector Regression (SVR) based approach to resolve the problem. Results show its efficiency and superiority compared to the state of the art.

Original language	English
Title of host publication	Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
Editors	Yuanqing Li, Derong Liu, Shengli Xie, El-Sayed M. El-Alfy, Dongbin Zhao
Publisher	Springer Verlag
Pages	883-892
Number of pages	10
ISBN (Print)	9783319700861
DOIs	https://doi.org/10.1007/978-3-319-70087-8_90
Publication status	Published - 1 Jan 2017
Externally published	Yes
Event	24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China Duration: 14 Nov 2017 → 18 Nov 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10634 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th International Conference on Neural Information Processing, ICONIP 2017
Country/Territory	China
City	Guangzhou
Period	14/11/17 → 18/11/17

Keywords

Machine learning
Resource management
Support vector regression
Virtual network function

Access to Document

10.1007/978-3-319-70087-8_90

Cite this

Jmila, H., Khedher, M. I., & El Yacoubi, M. A. (2017). Estimating VNF resource requirements using machine learning techniques. In Y. Li, D. Liu, S. Xie, E.-S. M. El-Alfy, & D. Zhao (Eds.), Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings (pp. 883-892). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10634 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-70087-8_90

Jmila, Houda ; Khedher, Mohamed Ibn ; El Yacoubi, Mounim A. / Estimating VNF resource requirements using machine learning techniques. Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. editor / Yuanqing Li ; Derong Liu ; Shengli Xie ; El-Sayed M. El-Alfy ; Dongbin Zhao. Springer Verlag, 2017. pp. 883-892 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Estimating VNF resource requirements using machine learning techniques",

abstract = "Resource Management in the network function virtualization (NFV) environment is a challenging task. The continuously varying demands of virtual network functions (VNF) call for dynamic algorithms to efficiently scale the allocated resources and meet fluctuating needs. In this context, studying the behavior of a VNF as a function of its environment helps to model its resource requirements and thus allocate them dynamically. This paper investigates the use of machine learning techniques to estimate VNFs needs in term of CPU as a function of the traffic they will process. We propose and adapt a Support Vector Regression (SVR) based approach to resolve the problem. Results show its efficiency and superiority compared to the state of the art.",

keywords = "Machine learning, Resource management, Support vector regression, Virtual network function",

author = "Houda Jmila and Khedher, \{Mohamed Ibn\} and \{El Yacoubi\}, \{Mounim A.\}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 24th International Conference on Neural Information Processing, ICONIP 2017 ; Conference date: 14-11-2017 Through 18-11-2017",

year = "2017",

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doi = "10.1007/978-3-319-70087-8\_90",

language = "English",

isbn = "9783319700861",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "883--892",

editor = "Yuanqing Li and Derong Liu and Shengli Xie and El-Alfy, \{El-Sayed M.\} and Dongbin Zhao",

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Jmila, H, Khedher, MI & El Yacoubi, MA 2017, Estimating VNF resource requirements using machine learning techniques. in Y Li, D Liu, S Xie, E-SM El-Alfy & D Zhao (eds), Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10634 LNCS, Springer Verlag, pp. 883-892, 24th International Conference on Neural Information Processing, ICONIP 2017, Guangzhou, China, 14/11/17. https://doi.org/10.1007/978-3-319-70087-8_90

Estimating VNF resource requirements using machine learning techniques. / Jmila, Houda; Khedher, Mohamed Ibn; El Yacoubi, Mounim A.
Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. ed. / Yuanqing Li; Derong Liu; Shengli Xie; El-Sayed M. El-Alfy; Dongbin Zhao. Springer Verlag, 2017. p. 883-892 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10634 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Estimating VNF resource requirements using machine learning techniques

AU - Jmila, Houda

AU - Khedher, Mohamed Ibn

AU - El Yacoubi, Mounim A.

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Resource Management in the network function virtualization (NFV) environment is a challenging task. The continuously varying demands of virtual network functions (VNF) call for dynamic algorithms to efficiently scale the allocated resources and meet fluctuating needs. In this context, studying the behavior of a VNF as a function of its environment helps to model its resource requirements and thus allocate them dynamically. This paper investigates the use of machine learning techniques to estimate VNFs needs in term of CPU as a function of the traffic they will process. We propose and adapt a Support Vector Regression (SVR) based approach to resolve the problem. Results show its efficiency and superiority compared to the state of the art.

AB - Resource Management in the network function virtualization (NFV) environment is a challenging task. The continuously varying demands of virtual network functions (VNF) call for dynamic algorithms to efficiently scale the allocated resources and meet fluctuating needs. In this context, studying the behavior of a VNF as a function of its environment helps to model its resource requirements and thus allocate them dynamically. This paper investigates the use of machine learning techniques to estimate VNFs needs in term of CPU as a function of the traffic they will process. We propose and adapt a Support Vector Regression (SVR) based approach to resolve the problem. Results show its efficiency and superiority compared to the state of the art.

KW - Machine learning

KW - Resource management

KW - Support vector regression

KW - Virtual network function

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DO - 10.1007/978-3-319-70087-8_90

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AN - SCOPUS:85035111623

SN - 9783319700861

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 883

EP - 892

BT - Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings

A2 - Li, Yuanqing

A2 - Liu, Derong

A2 - Xie, Shengli

A2 - El-Alfy, El-Sayed M.

A2 - Zhao, Dongbin

PB - Springer Verlag

T2 - 24th International Conference on Neural Information Processing, ICONIP 2017

Y2 - 14 November 2017 through 18 November 2017

ER -

Jmila H, Khedher MI, El Yacoubi MA. Estimating VNF resource requirements using machine learning techniques. In Li Y, Liu D, Xie S, El-Alfy ESM, Zhao D, editors, Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Springer Verlag. 2017. p. 883-892. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-70087-8_90

Estimating VNF resource requirements using machine learning techniques

Abstract

Publication series

Conference

Keywords

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Cite this