SDN/NFV, Machine Learning, and Big Data Driven Network Slicing for 5G

Luong Vy Le, Bao-Shuh Lin , Li-Ping Tung, Do Sinh

研究成果: Conference contribution同行評審

37 引文 斯高帕斯(Scopus)

摘要

5G networks are expected to be able to satisfy a variety of vertical services for mobile users, business demands, and automotive industry. Network slicing is a promising technology for 5G to provide a network as a service (NaaS) for a wide range of services that run on different virtual networks deployed on a shared network infrastructure. Moreover, the SON (self-organizing network) in 5G is expected as a significant evolution to guarantee for full intelligence, automatic, and faster management and optimization. To deal with those requirements, recently, software-defined networking (SDN), network functions virtualization (NFV), big data, and machine learning have been proposed as emerging technologies and the necessary tools for 5G, especially, for network slicing. This study aims to integrate various machine learning (ML) algorithms, big data, SDN, and NFV to build a comprehensive architecture and an experimental framework for the future SONs and network slicing. Finally, based on this framework, we successfully implemented an early state traffic classification and network slicing for mobile broadband traffic applications implemented at Broadband Mobile Lab (BML), National Chiao Tung University (NCTU).

原文American English
主出版物標題IEEE 5G World Forum, 5GWF 2018 - Conference Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面20-25
頁數6
ISBN(電子)9781538649824
DOIs
出版狀態Published - 9 7月 2018
事件1st IEEE 5G World Forum, 5GWF 2018 - Santa Clara, United States
持續時間: 9 7月 201811 7月 2018

出版系列

名字IEEE 5G World Forum, 5GWF 2018 - Conference Proceedings

Conference

Conference1st IEEE 5G World Forum, 5GWF 2018
國家/地區United States
城市Santa Clara
期間9/07/1811/07/18

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