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Approach for minimising network effect of VNF migration

为最小化 VNF 移植的网络效果来临

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

作者机构:Northeastern Univ Coll Software Shenyang 110169 Liaoning Peoples R China Northeastern Univ Coll Comp Sci & Engn Shenyang 110169 Liaoning Peoples R China Northeastern Univ State Key Lab Automat Proc Ind Coll Informat Sci & Engn 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:2574-2581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 

主  题:software defined networking virtual machines resource allocation virtualisation telecommunication network topology software defined network environment network function virtualisation virtual machine migration virtual network function migration migration time migration cost network topology VNF migration migration effect network load network effect minimization network load balancing VNF 

摘      要:In the software defined network (SDN) environment, network function virtualisation enables the virtual machine migration. Owing to the fact that transferring large amount of data will impede competing workflows, virtual network function (VNF) migration has brought a new perspective. Many optimised algorithms focusing on limiting migration time and migration cost have been proposed. In this study, the authors address the problem from a different perspective. They view the network topology from a global perspective and focus on the network effect of the whole network caused by VNF migration in the context of SDN. They introduce a parameter delay to formulate the network effect and an effect model is proposed to evaluate the migration effect of the network. In addition, a heuristic algorithm is proposed to minimise network effect while balancing network load and improving the service considering the migration cost and resources limit at the same time. The practicability and efficiency of the proposed model and algorithm are validated by simulation evaluation. By comparing their proposed algorithm with traditional benchmarks and closely related benchmarks, the experimental results show that their proposed algorithm largely reduces the network effect, while at the same time limiting the run time.

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