Real-Time Virtual Network Function (VNF) Migration toward Low Network Latency in Cloud Environments

2017 ◽  
Author(s):  
Daewoong Cho ◽  
Javid Taheri ◽  
Albert Y. Zomaya ◽  
Pascal Bouvry
Keyword(s):  
Real Time ◽  
Virtual Network ◽  
Network Function ◽  
Network Latency ◽  
Cloud Environments ◽  
Virtual Network Function

scholarly journals Virtual network function embedding in real cloud environments

2015 ◽  
Vol 93 ◽  
pp. 506-517 ◽  
Author(s):  
Paolo Bellavista ◽  
Franco Callegati ◽  
Walter Cerroni ◽  
Chiara Contoli ◽  
Antonio Corradi ◽  
...  
Keyword(s):  
Virtual Network ◽  
Network Function ◽  
Cloud Environments ◽  
Virtual Network Function

scholarly journals Deep Reinforcement Learning-Based Algorithm for VNF-SC Deployment

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Junlei Xuan ◽  
Huifang Yang ◽  
Xuelin Zhao ◽  
Xingpo Ma ◽  
Xiaokai Yang
Keyword(s):  
Real Time ◽  
Optimization Model ◽  
Virtual Network ◽  
Network Function Virtualization ◽  
Prediction Algorithm ◽  
Network Service ◽  
The Real ◽  
Service Chain ◽  
Network Function ◽  
Virtual Network Function

Network function virtualization (NFV) has the potential to lead to significant reductions in capital expenditure and can improve the flexibility of the network. Virtual network function (VNF) deployment problem will be one of key problems that need to be addressed in NFV. To solve the problem of routing and VNF deployment, an optimization model, which minimizes the maximum index of used frequency slots, the number of used frequency slots, and the number of initialized VNF, is established. In this optimization model, the dependency among the different VNFs is considered. In order to solve the service chain mapping problem of high dynamic virtual network, a new virtual network function service chain mapping algorithm PDQN-VNFSC was proposed by combining prediction algorithm and DQN (Deep Q-Network). Firstly, the real-time mapping of virtual network service chains is modeled into a partial observable Markov decision process. Then, the real-time mapping process of virtual network service chain is optimized by using global and long-term benefits. Finally, the service chain of virtual network function is mapped through the learning decision framework of offline learning and online deployment. The simulation results show that, compared with the existing algorithms, the proposed algorithm has a lower the maximum index of used frequency slots, the number of used frequency slots, and the number of initialized VNF.

An evaluation of a virtual network function for real-time threat detection using stream processing

2018 ◽  
Author(s):  
Martin Andreoni Lopez ◽  
Antonio Gonzalez Pastana Lobato ◽  
Otto Carlos M. B. Duarte ◽  
Guy Pujolle
Keyword(s):  
Real Time ◽  
Stream Processing ◽  
Virtual Network ◽  
Threat Detection ◽  
Network Function ◽  
Virtual Network Function

Graph Neural Network-based Virtual Network Function Deployment Prediction

2020 ◽  
Author(s):  
Hee-Gon Kim ◽  
Suhyun Park ◽  
Dongnyeong Heo ◽  
Stanislav Lange ◽  
Heeyoul Choi ◽  
...  
Keyword(s):  
Neural Network ◽  
Virtual Network ◽  
Network Function ◽  
Virtual Network Function

Virtual network function placement with bounded migrations

2021 ◽  
Author(s):  
Yanghao Xie ◽  
Sheng Wang ◽  
Binbin Wang
Keyword(s):  
Virtual Network ◽  
Network Function ◽  
Virtual Network Function
Sign in / Sign up

Export Citation Format

Share Document