人工智能在网络编排系统中的应用

陈天骄; 刘江; 黄韬

doi:10.11959/j.issn.1000-0801.2019095

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人工智能在网络编排系统中的应用

专题：智能通信技术及应用 | 更新时间：2024-06-05

- 人工智能在网络编排系统中的应用
- Application of artificial intelligence in network orchestration system
- 电信科学 2019年35卷第5期页码：9-16
- 作者机构：
- 作者简介：
  
  [ "陈天骄（1996- ），男，北京邮电大学博士生，主要研究方向为未来网络体系架构、空间信息网络、网络资源调度。" ]
  [ "刘江（1983- ），男，博士，北京邮电大学副教授，主要研究方向为未来网络体系架构、网络虚拟化、软件定义网络、信息中心网络等。" ]
  [ "黄韬（1980- ），男，博士，北京邮电大学教授，主要研究方向为未来网络体系架构、路由转发、网络虚拟化、软件定义网络等。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61671086);中国工程院咨询研究项目;The Consulting Project of Chinese Academy of Engineering(2019-XY-5)
- DOI：10.11959/j.issn.1000-0801.2019095
  中图分类号： TP393
- 网络出版日期：2019-05，
  
  纸质出版日期：2019-05-20
- 稿件说明：
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陈天骄, 刘江, 黄韬. 人工智能在网络编排系统中的应用[J]. 电信科学, 2019,35(5):9-16.

Tianjiao CHEN, Jiang LIU, Tao HUANG. Application of artificial intelligence in network orchestration system[J]. Telecommunications science, 2019, 35(5): 9-16.
陈天骄, 刘江, 黄韬. 人工智能在网络编排系统中的应用[J]. 电信科学, 2019,35(5):9-16. DOI： 10.11959/j.issn.1000-0801.2019095.

Tianjiao CHEN, Jiang LIU, Tao HUANG. Application of artificial intelligence in network orchestration system[J]. Telecommunications science, 2019, 35(5): 9-16. DOI： 10.11959/j.issn.1000-0801.2019095.

摘要

基于软件定义网络和网络虚拟化，网络编排可以提供快速、高效的差异化业务部署的能力。随着人工智能技术在各领域的广泛应用，网络编排系统与人工智能的结合成为各大运营商和厂商的研究重点。首先介绍了编排系统的概念和各大网络编排开源组织的发展现状；接着在设计态和运行态的架构上引出了基于人工智能的网络编排架构，实现“自动化”到“智能化”的转变；最后对人工智能在编排系统中SDN、NFV和运维方面的应用进行总结，并提出了人工智能在编排系统中未来的发展方向。

Abstract

Based on software defined networking and network virtualization

network orchestration can deploy differentiated services quickly and efficiently.With the wide application of artificial intelligence technology

the combination of network orchestration system and artificial intelligence has become the research focus of major operators and manufacturers.Firstly

the concept of network orchestration system and the development status of major open source organizations were introduced.Then

based on the design time and the run time

the network orchestration architecture using artificial intelligence was introduced to realize the transformation from “automation” to “intelligence”.Finally

the application of artificial intelligence in SDN

NFV and operation and maintenance in the orchestration system was summarized

and the future development direction was proposed.

关键词

Keywords

references

QIANG Y , WEIHUA Z , XU L , et al . End-to-end delay modeling for embedded VNF chains in 5G core networks [J ] . IEEE Internet of Things Journal , 2018 :1.

ORDONEZ-LUCENA J , AMEIGEIRAS P , LOPEZ D , et al . Network slicing for 5G with SDN/NFV:concepts,architectures and challenges [J ] . IEEE Communications Magazine , 2017 , 55 ( 5 ): 80 - 87 .

ZHANG X , HUANG Z , WU C , et al . Online stochastic buy-sell mechanism for VNF chains in the NFV market [J ] . IEEE Journal on Selected Areas in Communications , 2017 , 35 ( 2 ): 392 - 406 .

张晨 , 黄韬 , 张娇 , 等 . 网络编排技术进展研究 [J ] . 信息通信技术 , 2016 ( 1 ): 68 - 76 .

ZHANG C , HUANG T , ZHANG J , et al . A technology research of network orchestration [J ] . Information and Communications Technologies , 2016 ( 1 ): 68 - 76 .

SLIM F , GUILLEMIN F , GRAVEY A , et al . Towards a dynamicadaptive placement of virtual network functions under ONAP [C ] // 2017 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN),Nov 6-8,2017,Berlin,Germany . Piscataway:IEEE Press , 2017 .

Open Source MANO [Z ] .

赵鹏 , 段晓东 . SDN/NFV 发展中的关键:编排器的发展与挑战 [J ] . 电信科学 , 2017 , 33 ( 4 ): 18 - 25 .

ZHAO P , DUAN X D . Key of SDN/NFV development:development and challenge of orchestrator [J ] . Telecommunications Science , 2017 , 33 ( 4 ): 18 - 25 .

ONAP [Z ] . 2019

DRAGONI N , LANESE I , LARSEN S T , et al . Microservices:how to make your application scale [C ] // 11th International Andrei Ershov Memorial Conference on Perspectives of System Informatics,June 27-29,2017,Moscow,Russia . Berlin:Springer , 2017 .

HAERI S , TRAJKOVIC L . Virtual network embedding via Montec Arlo tree search [J ] . IEEE Transactions on Cybernetics , 2017 : 1 - 12 .

BLENK A , KALLMBACH P , ZERWAS J , et al . NeuroViNE:a neural preprocessor for your virtual network embedding algorithm [C ] // IEEE INFOCOM,April 15-19,2018,Honolulu,USA . Piscataway:IEEE Press , 2018 .

MAO H , ALIZAEH M , ISHAI M , et al . Resource management with deep reinforcement learning [C ] // The 15th ACM Workshop,November 9-10,2016,Atlanta,GA,USA . New York:ACM Press , 2016 .

SIEBER C , BASTA A , BLENK A , et al . Online resource mapping for SDN network hypervisors using machine learning [C ] // 2016 IEEE NetSoft Conference and Workshops (NetSoft),June 6-10,2016,Seoul,Korea . Piscataway:IEEE Press , 2016 .

HE M , KALMBACH P , BLENK A , et al . Algorithm-data driven optimization of adaptive communication networks [C ] // IEEE 25 th International Conference on Network Protocols,Oct 10-13,2017,Toronto,ON,Canada . Piscataway:IEEE Press , 2017 .

XIE J , YU F R , HUANG T , et al . A survey of machine learning techniques applied to software defined networking (SDN):research issues and challenges [J ] . IEEE Communications Surveys＆ Tutorials , 2018 ( 99 ):1.

PASCA S T V , KODALI S S P , KATAOKA K . AMPS:application aware multipath flow routing using machine learning in SDN [C ] // 2017 Twenty-third National Conference on Communications (NCC),March 2-4,2017,Chennai,India . Piscataway:IEEE Press , 2017 .

AZZOUNI A , PUJOLLE G . NeuTM:aneural network-based framework for traffic matrix prediction in SDN [J ] . arXiv:1709.07080 , 2017 .

STAMPA G , ARIAS M,SANCHEZ-HARLES D , et al . A deep-reinforcement learning approach for software-definednetworking routing optimization [J ] . arXiv:1709.07080 , 2017 .

DORO S , GALLUCCIO L , PALAZZO S , et al . A game theoretic approach for distributed resource allocation and orchestration of softwarized networks [J ] . IEEE Journal on Selected Areas in Communications , 2017 , 35 ( 3 ): 721 - 735 .

SHI R , ZHANG J , CHU W , et al . MDP and machine learning-based cost-optimization of dynamic resource allocation for network function virtualization [C ] // 2015 IEEE International Conference on Services Computing (SCC),June 27-July 2,2015,New York,USA . Piscataway:IEEE Press , 2015 .

KIM S I , KIM H S . A research on dynamic service function chaining based on reinforcement learning using resource usage [C ] // IEEE 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN),July 4-7,2017,Milan,Italy . Piscataway:IEEE Press , 2017 .

VERGARA-REYES J , MARTINEZ-ORDONEZ M C , ORDONEZ A , et al . IP traffic classification in NFV:A benchmarking of supervised Machine Learning algorithms [C ] // 2017 IEEE Colombian Conference on Communications and Computing (COLCOM),August 16-18,2017,Cartagena,Colombia . Piscataway:IEEE Press , 2017 .

卜超 , 王兴伟 , 黄敏 . 自适应路由服务合成:模型及优化 [J ] . 软件学报 , 2017 ( 9 ).

BU C , WANG X W , HUANG M . Adaptive routing service composition:modeling and optimization [J ] . Journal of Software , 2017 ( 9 ).

HURLEY T , PERDOMO J E , PEREZ-PONS A . HMM-based intrusion detection system for software defined networking [C ] // IEEE International Conference on Machine Learning ＆ Applications,Dec 18-21,2017,Cancun,Mexico . Piscataway:IEEE Press , 2017 .

SHONE N , NGOC T N , PHAI V D , et al . A deep learning approach to network intrusion detection [J ] . IEEE Transactions on Emerging Topics in Computational Intelligence , 2018 , 2 ( 1 ): 41 - 50 .

YUSOF M A M , ALI F H M , DARUS M Y . Detection and defense algorithms of different types of DDoS attacks using machine learning [J ] . International Journal of Engineering and Technology , 2017 , 9 ( 5 ).

SURESH M , ANITHA R . Evaluating machine learning algorithms for detecting DDoS attacks [C ] // International Conference on Network Security and Applications,July 15-17,2011,Chennai,India . Berlin:Springer , 2011 .

NANDA S , ZAFARI F , DECUSATIS C , et al . Predicting network attack patterns in SDN using machine learning approach [C ] // Network Function Virtualization ＆ Software Defined Networks,Nove 6-8,2017,Berlin,Germany . Piscataway:IEEE Press , 2017 .

BENAYAS F , CARRERA A , IGLESIAS C A . Towards an autonomic Bayesian fault diagnosis service for SDN environments based on a big data infrastructure [C ] // 2018 Fifth International Conference on Software Defined Systems (SDS),April 23-26,2018,Barcelona,Spain . Piscataway:IEEE Press , 2018 .

JAGADEESAN L J , MENDIRATTA V . Programming the network:application software faults in software-defined networks [C ] // 2016 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW),Oct 23-27,2016,Ottawa,ON,Canada . Piscataway:IEEE Press , 2016 .

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