TY - GEN
T1 - SDN/NFV, Machine Learning, and Big Data Driven Network Slicing for 5G
AU - Le, Luong Vy
AU - Lin , Bao-Shuh
AU - Tung, Li-Ping
AU - Sinh, Do
PY - 2018/7/9
Y1 - 2018/7/9
N2 - 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).
AB - 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).
KW - 5G
KW - Application identification.
KW - Big Data
KW - Machine Learning
KW - Network slicing
KW - SDN/NFV
KW - SON
UR - http://www.scopus.com/inward/record.url?scp=85057109024&partnerID=8YFLogxK
U2 - 10.1109/5GWF.2018.8516953
DO - 10.1109/5GWF.2018.8516953
M3 - Conference contribution
AN - SCOPUS:85057109024
T3 - IEEE 5G World Forum, 5GWF 2018 - Conference Proceedings
SP - 20
EP - 25
BT - IEEE 5G World Forum, 5GWF 2018 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE 5G World Forum, 5GWF 2018
Y2 - 9 July 2018 through 11 July 2018
ER -