Estimating VNF Resource Requirements Using Machine Learning Techniques

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.

[1]  Fernando M. V. Ramos,et al.  Software-Defined Networking: A Comprehensive Survey , 2014, Proceedings of the IEEE.

[2]  Jean C. Walrand,et al.  Knowledge-Defined Networking: Modelització de la xarxa a través de l’aprenentatge automàtic i la inferència , 2016 .

[3]  Juan Felipe Botero,et al.  Resource Allocation in NFV: A Comprehensive Survey , 2016, IEEE Transactions on Network and Service Management.

[4]  Raouf Boutaba,et al.  Topology-Aware Prediction of Virtual Network Function Resource Requirements , 2017, IEEE Transactions on Network and Service Management.

[5]  Raouf Boutaba,et al.  A connectionist approach to dynamic resource management for virtualised network functions , 2016, 2016 12th International Conference on Network and Service Management (CNSM).

[6]  Joan Serrat,et al.  Management and orchestration challenges in network functions virtualization , 2016, IEEE Communications Magazine.

[7]  Steven L. Salzberg,et al.  Book Review: C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993 , 1994, Machine Learning.

[8]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[9]  I. Jolliffe.  Principal Component Analysis , 2002 .

[10]  Ah Chung Tsoi,et al.  The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.

[11]  Filip De Turck,et al.  Network Function Virtualization: State-of-the-Art and Research Challenges , 2015, IEEE Communications Surveys & Tutorials.

[12]  Djamal Zeghlache,et al.  A self-stabilizing framework for dynamic bandwidth allocation in virtual networks , 2016, NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium.

[13]  Georgios Xilouris,et al.  Automated generation of VNF deployment rules using infrastructure affinity characterization , 2016, 2016 IEEE NetSoft Conference and Workshops (NetSoft).

[14]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[15]  Viswanathan Arunachalam,et al.  Stochastic modeling for delay analysis of a VoIP network , 2015, Ann. Oper. Res..

[16]  Jia Zhang,et al.  MDP and Machine Learning-Based Cost-Optimization of Dynamic Resource Allocation for Network Function Virtualization , 2015, 2015 IEEE International Conference on Services Computing.

[17]  Mohamed Ibn Khedher,et al.  Two-Stage Filtering Scheme for Sparse Representation Based Interest Point Matching for Person Re-identification , 2015, ACIVS.

[18]  Chris Metz,et al.  COLAP: A predictive framework for service function chain placement in a multi-cloud environment , 2017, 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC).