scholarly journals A Genetic Algorithm for VNF Provisioning in NFV-Enabled Cloud/MEC RAN Architectures

2018 ◽  
Vol 8 (12) ◽  
pp. 2614 ◽  
Author(s):  
Lidia Ruiz ◽  
Ramón Durán ◽  
Ignacio de Miguel ◽  
Pouria Khodashenas ◽  
Jose-Juan Pedreño-Manresa ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Optical Network ◽  
Network Capacity ◽  
Network Function Virtualization ◽  
Learning Stage ◽  
Service Chain ◽  
Network Function ◽  
Radio Access ◽  
Active Core ◽  
5G Network

5G technologies promise to bring new network and service capacities and are expected to introduce significant architectural and service deployment transformations. The Cloud-Radio Access Networks (C-RAN) architecture, enabled by the combination of Software Defined Networking (SDN), Network Function Virtualization (NFV) and Mobile Edge Computing (MEC) technologies, play a key role in the development of 5G. In this context, this paper addresses the problems of Virtual Network Functions (VNF) provisioning (VNF-placement and service chain allocation) in a 5G network. In order to solve that problem, we propose a genetic algorithm that, considering both computing resources and optical network capacity, minimizes both the service blocking rate and CPU usage. In addition, we present an algorithm extension that adds a learning stage and evaluate the algorithm performance benefits in those scenarios where VNF allocations can be reconfigured. Results reveal and quantify the advantages of reconfiguring the VNF mapping depending on the current demands. Our methods outperform previous proposals in the literature, reducing the service blocking ratio while saving energy by reducing the number of active core CPUs.

ONAP

2021 ◽  
pp. 212-249
Author(s):  
Eric Debeau ◽  
Veronica Quintuna-Rodriguez
Keyword(s):  
Network Function Virtualization ◽  
Network Function ◽  
On Demand ◽  
Public Networks ◽  
5G Network ◽  
Network Flexibility ◽  
Optimal Resource ◽  
Network Functions ◽  
Lifecycle Management ◽  
Automation Techniques

The ever-increasing complexity of networks and services advocates for the introduction of automation techniques to facilitate the design, the delivery, and the operation of such networks and services. The emergence of both network function virtualization (NFV) and software-defined networks (SDN) enable network flexibility and adaptability which open the door to on-demand services requiring automation. In aim of holding the increasing number of customized services and the evolved capabilities of public networks, the open network automation platform (ONAP), which is in open source, particularly addresses automation techniques while enabling dynamic orchestration, optimal resource allocation capabilities, and end-to-end service lifecycle management. This chapter addresses the key ONAP features that can be used by industrials and operators to automatically manage and orchestrate a wide set of services ranging from elementary network functions (e.g., firewalls) to more complex services (e.g., 5G network slices).

scholarly journals Intent-Based End-to-End Network Service Orchestration System for Multi-Platforms

2020 ◽  
Vol 12 (7) ◽  
pp. 2782 ◽  
Author(s):  
Adeel Rafiq ◽  
Asif Mehmood ◽  
Talha Ahmed Khan ◽  
Khizar Abbas ◽  
Muhammad Afaq ◽  
...  
Keyword(s):  
Human Error ◽  
Human Interaction ◽  
Network Function Virtualization ◽  
Network Service ◽  
Service Orchestration ◽  
Network Function ◽  
5G Network ◽  
Level Information ◽  
End To End ◽  
High Level

On-demand service is the main feature of the 5G network, and Network Function Virtualization (NFV) provides it by virtualizing the existing 5G network infrastructure. NFV crafts various virtual networks on a shared physical network, but one of the core challenges in future 5G networks is to automate the modeling of Virtualized Network Functions (VNFs) and end-to-end Network Service (NS) orchestration with less human interaction. Traditionally, the descriptor of VNF and NS is created manually, which requires expert-level skills. This manual approach has a big threat of human error, which can be avoided by using the Intent-Based Networking (IBN) approach. The IBN approach eliminates the requirement of expertise for designing VNFs and NS by taking users’ intentions as an input. In this paper, the proposed system presents the Intent Management System for VNF modeling and end-to-end NS orchestration for multi-platforms. This system takes the high-level information related to a specific service, configures it accordingly, and converts it into the selected platform. The proposed system is tested using Mobile Central Office Re-architected as Data Center (M-CORD) and Open-Source Management and Orchestration (OSM) orchestrators. The results section shows that the proposed system reduces the effort of the end-user in creating network slices and provides seamless end-to-end service orchestration.

scholarly journals Securing Virtual Network Function (VNF) in Telco Cloud

2020 ◽  
Author(s):  
Bharathkumar Ravichandran
Keyword(s):  
General Purpose ◽  
Virtual Network ◽  
Network Function Virtualization ◽  
Communication Architecture ◽  
Network Function ◽  
Fifth Generation ◽  
Security Challenges ◽  
5G Network ◽  
Network Functions ◽  
Computing Platforms

In the fifth generation mobile communication architecture (5G), network functions which traditionally existed as discrete hardware entities based on custom architectures, are replaced with dynamic, scalable Virtual Network Functions (VNF) that run on general purpose (x86) cloud computing platforms, under the paradigm Network Function Virtualization (NFV). The shift towards a virtualized infrastructure poses its own set of security challenges that need to be addressed. One such challenge that we seek to address in this paper is providing integrity, authenticity and confidentiality protection for VNFs.

5G: rethink mobile communications for 2020+

2016 ◽  
Vol 374 (2062) ◽  
pp. 20140432 ◽  
Author(s):  
I. Chih-Lin ◽  
Shuangfeng Han ◽  
Zhikun Xu ◽  
Qi Sun ◽  
Zhengang Pan
Keyword(s):  
Mobile Communications ◽  
Access Network ◽  
High Mobility ◽  
High Energy ◽  
Network Function Virtualization ◽  
Traffic Density ◽  
Network Function ◽  
High Data ◽  
5G Network ◽  
User Centric

The 5G network is anticipated to meet the challenging requirements of mobile traffic in the 2020s, which are characterized by super high data rate, low latency, high mobility, high energy efficiency and high traffic density. This paper provides an overview of China Mobile’s 5G vision and potential solutions. Three key characteristics of 5G are analysed, i.e. super fast, soft and green. The main 5G R&D themes are further elaborated, which include five fundamental rethinkings of the traditional design methodologies. The 5G network design considerations are also discussed, with cloud radio access network, ultra-dense network, software defined network and network function virtualization examined as key potential solutions towards a green and soft 5G network. The paradigm shift to user-centric network operation from the traditional cell-centric operation is also investigated, where the decoupled downlink and uplink, control and data, and adaptive multiple connections provide sufficient means to achieve a user-centric 5G network with ‘no more cells’. The software defined air interface is investigated under a uniform framework and can adaptively adapt the parameters to well satisfy various requirements in different 5G scenarios.

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.

scholarly journals Application of Machine Learning Algorithms for Traffic Forecasting in Dynamic Optical Networks with Service Function Chains

2020 ◽  
Vol 45 (3) ◽  
pp. 217-232
Author(s):  
Daniel Szostak ◽  
Krzysztof Walkowiak
Keyword(s):  
Machine Learning ◽  
Optical Networks ◽  
Network Traffic ◽  
Optical Network ◽  
Machine Learning Algorithms ◽  
Network Function Virtualization ◽  
Service Function ◽  
Traffic Forecasting ◽  
Network Function ◽  
Traffic Characteristics

AbstractKnowledge about future optical network traffic can be beneficial for network operators in terms of decreasing an operational cost due to efficient resource management. Machine Learning (ML) algorithms can be employed for forecasting traffic with high accuracy. In this paper we describe a methodology for predicting traffic in a dynamic optical network with service function chains (SFC). We assume that SFC is based on the Network Function Virtualization (NFV) paradigm. Moreover, other type of traffic, i.e. regular traffic, can also occur in the network. As a proof of effectiveness of our methodology we present and discuss numerical results of experiments run on three benchmark networks. We examine six ML classifiers. Our research shows that it is possible to predict a future traffic in an optical network, where SFC can be distinguished. However, there is no one universal classifier that can be used for each network. Choice of an ML algorithm should be done based on a network traffic characteristics analysis.

scholarly journals Optimized Energy Aware 5G Network Function Virtualization

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 44939-44958 ◽  
Author(s):  
Ahmed N. Al-Quzweeni ◽  
Ahmed Q. Lawey ◽  
Taisir E. H. Elgorashi ◽  
Jaafar M. H. Elmirghani
Keyword(s):  
Network Function Virtualization ◽  
Energy Aware ◽  
Network Function ◽  
5G Network
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