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

Akihiro NAKAO, Ping DU
2018 IEICE transactions on communications  
Akihiro NAKAO †a) and Ping DU †b) , Members SUMMARY 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
more » ... ial 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. key words: software-defined networking (SDN), network functions virtualisation (NFV), network virtualization, 5G, network slicing Manuscript
doi:10.1587/transcom.2017cqi0002 fatcat:b24n5zc46vboxnbzsnntl3sfqu