Journal
IEICE TRANSACTIONS ON COMMUNICATIONS
Volume E101B, Issue 7, Pages 1536-1543Publisher
IEICE-INST ELECTRONICS INFORMATION COMMUNICATION ENGINEERS
DOI: 10.1587/transcom.2017CQI0002
Keywords
software-defined networking (SDN); network functions virtualisation (NFV); network virtualization; 5G; network slicing
Funding
- Grants-in-Aid for Scientific Research [15H01681] Funding Source: KAKEN
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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.
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