Optimised approach for VNF embedding in NFV

作     者：Yi, Bo Wang, Xingwei Huang, Min 

作者机构：Northeastern Univ Coll Comp Sci & Engn Shenyang 110169 Liaoning Peoples R China Northeastern Univ Coll Software Shenyang 110169 Liaoning Peoples R China Northeastern Univ Coll Informat Sci & Engn State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China 

出 版 物：《IET COMMUNICATIONS》 (IET通信)

年 卷 期：2018年第12卷第20期

页      面：2630-2638页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：National Natural Science Foundation of China Major International (Regional) Joint Research Project of NSFC National Science Foundation for Distinguished Young Scholars of China Foundation for Innovative Research Groups of National Science Foundation of China MoE [MCM20160201] Program for Liaoning Innovative Research Term in University [LT2016007] ChinaMobile [MCM20160201] 

主　　题：integer programming virtualisation telecommunication network topology linear programming optimised approach VNF embedding NFV Virtual Network Function embedding problem Network Function Virtualisation NP-hard Integer Linear Programming model ILP model global network connectivity local substrate node capacity real-world network topologies 

摘      要：The Virtual Network Function (VNF) embedding problem is important for service provision in the context of Network Function Virtualisation (NFV). However, this problem is proved to be NP-hard and challenging, and requires to be explored further. In this study, the authors first formulate it as an Integer Linear Programming (ILP) model for optimal solutions. Then, to compensate for the high running time of solving the ILP model, they propose a heuristic approach which fulfils the embedding process by jointly taking the global network connectivity and the local substrate node capacity into consideration. The simulation on real-world network topologies demonstrates that the proposed approach can provide solutions within 1.7 times of the optimal solution offered by ILP. In addition, the experiments also suggest that the proposed approach can provide up to 2.75 times reduction in the overall cost than the other benchmarks.