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Virtual Network Embedding for Multi-Domain Heterogeneous Converged Optical Networks: Issues and Challenges
The emerging 5G applications and the connectivity of billions of devices have driven the investigation of multi-domain heterogeneous converged optical networks. To support emerging applications with their diverse quality of service requirements, network slicing has been proposed as a promising techn...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248854/ https://www.ncbi.nlm.nih.gov/pubmed/32384762 http://dx.doi.org/10.3390/s20092655 |
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author | Zong, Yue Feng, Chuan Guan, Yingying Liu, Yejun Guo, Lei |
author_facet | Zong, Yue Feng, Chuan Guan, Yingying Liu, Yejun Guo, Lei |
author_sort | Zong, Yue |
collection | PubMed |
description | The emerging 5G applications and the connectivity of billions of devices have driven the investigation of multi-domain heterogeneous converged optical networks. To support emerging applications with their diverse quality of service requirements, network slicing has been proposed as a promising technology. Network virtualization is an enabler for network slicing, where the physical network can be partitioned into different configurable slices in the multi-domain heterogeneous converged optical networks. An efficient resource allocation mechanism for multiple virtual networks in network virtualization is one of the main challenges referred as virtual network embedding (VNE). This paper is a survey on the state-of-the-art works for the VNE problem towards multi-domain heterogeneous converged optical networks, providing the discussion on future research issues and challenges. In this paper, we describe VNE in multi-domain heterogeneous converged optical networks with enabling network orchestration technologies and analyze the literature about VNE algorithms with various network considerations for each network domain. The basic VNE problem with various motivations and performance metrics for general scenarios is discussed. A VNE algorithm taxonomy is presented and discussed by classifying the major VNE algorithms into three categories according to existing literature. We analyze and compare the attributes of algorithms such as node and link embedding methods, objectives, and network architecture, which can give a selection or baseline for future work of VNE. Finally, we explore some broader perspectives in future research issues and challenges on 5G scenario, field trail deployment, and machine learning-based algorithms. |
format | Online Article Text |
id | pubmed-7248854 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-72488542020-06-10 Virtual Network Embedding for Multi-Domain Heterogeneous Converged Optical Networks: Issues and Challenges Zong, Yue Feng, Chuan Guan, Yingying Liu, Yejun Guo, Lei Sensors (Basel) Review The emerging 5G applications and the connectivity of billions of devices have driven the investigation of multi-domain heterogeneous converged optical networks. To support emerging applications with their diverse quality of service requirements, network slicing has been proposed as a promising technology. Network virtualization is an enabler for network slicing, where the physical network can be partitioned into different configurable slices in the multi-domain heterogeneous converged optical networks. An efficient resource allocation mechanism for multiple virtual networks in network virtualization is one of the main challenges referred as virtual network embedding (VNE). This paper is a survey on the state-of-the-art works for the VNE problem towards multi-domain heterogeneous converged optical networks, providing the discussion on future research issues and challenges. In this paper, we describe VNE in multi-domain heterogeneous converged optical networks with enabling network orchestration technologies and analyze the literature about VNE algorithms with various network considerations for each network domain. The basic VNE problem with various motivations and performance metrics for general scenarios is discussed. A VNE algorithm taxonomy is presented and discussed by classifying the major VNE algorithms into three categories according to existing literature. We analyze and compare the attributes of algorithms such as node and link embedding methods, objectives, and network architecture, which can give a selection or baseline for future work of VNE. Finally, we explore some broader perspectives in future research issues and challenges on 5G scenario, field trail deployment, and machine learning-based algorithms. MDPI 2020-05-06 /pmc/articles/PMC7248854/ /pubmed/32384762 http://dx.doi.org/10.3390/s20092655 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Zong, Yue Feng, Chuan Guan, Yingying Liu, Yejun Guo, Lei Virtual Network Embedding for Multi-Domain Heterogeneous Converged Optical Networks: Issues and Challenges |
title | Virtual Network Embedding for Multi-Domain Heterogeneous Converged Optical Networks: Issues and Challenges |
title_full | Virtual Network Embedding for Multi-Domain Heterogeneous Converged Optical Networks: Issues and Challenges |
title_fullStr | Virtual Network Embedding for Multi-Domain Heterogeneous Converged Optical Networks: Issues and Challenges |
title_full_unstemmed | Virtual Network Embedding for Multi-Domain Heterogeneous Converged Optical Networks: Issues and Challenges |
title_short | Virtual Network Embedding for Multi-Domain Heterogeneous Converged Optical Networks: Issues and Challenges |
title_sort | virtual network embedding for multi-domain heterogeneous converged optical networks: issues and challenges |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248854/ https://www.ncbi.nlm.nih.gov/pubmed/32384762 http://dx.doi.org/10.3390/s20092655 |
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