Virtual Network Embedding for Multi-Domain Heterogeneous Converged Optical Networks: Issues and Challenges.

converged optical networks machine learning network slicing software-defined network virtual network embedding

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
06 May 2020
Historique:
received: 13 04 2020
revised: 01 05 2020
accepted: 03 05 2020
entrez: 10 5 2020
pubmed: 10 5 2020
medline: 10 5 2020
Statut: epublish

Résumé

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.

Identifiants

pubmed: 32384762
pii: s20092655
doi: 10.3390/s20092655
pmc: PMC7248854
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Fundamental Research Funds for the Central Universities
ID : N161608001

Références

Opt Express. 2014 Feb 10;22(3):2595-602
pubmed: 24663552
IEEE Trans Cybern. 2018 Feb;48(2):510-521
pubmed: 28237939
Opt Express. 2018 Sep 17;26(19):24506-24530
pubmed: 30469567

Auteurs

Yue Zong (Y)

School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China.

Chuan Feng (C)

School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China.

Yingying Guan (Y)

School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China.

Yejun Liu (Y)

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

Lei Guo (L)

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

Classifications MeSH