Toward In-Network Deep Machine Learning for Identifying Mobile Applications and Enabling Application Specific Network Slicing

Akihiro NAKAO  Ping DU  

IEICE TRANSACTIONS on Communications   Vol.E101-B   No.7   pp.1536-1543
Publication Date: 2018/07/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2017CQI0002
Type of Manuscript: INVITED PAPER (Special Section on Communication Quality in Wireless Networks)
software-defined networking (SDN),  network functions virtualisation (NFV),  network virtualization,  5G,  network slicing,  

Full Text: FreePDF(2.9MB)

In this paper, we posit that, in future mobile network, network softwarization will be prevalent, and it becomes important to utilize deep machine learning within network to classify mobile traffic into fine grained slices, by identifying application types and devices so that we can apply Quality-of-Service (QoS) control, mobile edge/multi-access computing, and various network function per application and per device. This paper reports our initial attempt to apply deep machine learning for identifying application types from actual mobile network traffic captured from an MVNO, mobile virtual network operator and to design the system for classifying it to application specific slices.