IEICE TRANSACTIONS on Communications
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Toward In-Network Deep Machine Learning for Identifying Mobile Applications and Enabling Application Specific Network Slicing
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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 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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E101-B No.7 pp.1536-1543
- Publication Date
- 2018/07/01
- Publicized
- 2018/01/22
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2017CQI0002
- Type of Manuscript
- Special Section INVITED PAPER (Special Section on Communication Quality in Wireless Networks)
- Category
Authors
Akihiro NAKAO
The University of Tokyo
Ping DU
The University of Tokyo
Keyword
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Akihiro NAKAO, Ping DU, "Toward In-Network Deep Machine Learning for Identifying Mobile Applications and Enabling Application Specific Network Slicing" in IEICE TRANSACTIONS on Communications,
vol. E101-B, no. 7, pp. 1536-1543, July 2018, doi: 10.1587/transcom.2017CQI0002.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2017CQI0002/_p
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@ARTICLE{e101-b_7_1536,
author={Akihiro NAKAO, Ping DU, },
journal={IEICE TRANSACTIONS on Communications},
title={Toward In-Network Deep Machine Learning for Identifying Mobile Applications and Enabling Application Specific Network Slicing},
year={2018},
volume={E101-B},
number={7},
pages={1536-1543},
abstract={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.},
keywords={},
doi={10.1587/transcom.2017CQI0002},
ISSN={1745-1345},
month={July},}
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TY - JOUR
TI - Toward In-Network Deep Machine Learning for Identifying Mobile Applications and Enabling Application Specific Network Slicing
T2 - IEICE TRANSACTIONS on Communications
SP - 1536
EP - 1543
AU - Akihiro NAKAO
AU - Ping DU
PY - 2018
DO - 10.1587/transcom.2017CQI0002
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E101-B
IS - 7
JA - IEICE TRANSACTIONS on Communications
Y1 - July 2018
AB - 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.
ER -
English
