IEICE TRANSACTIONS on Communications
A Machine Learning Model for Wide Area Network Intelligence with Application to Multimedia Service
Summary :
Network intelligence is a discipline that builds on the capabilities of network systems to act intelligently by the usage of network resources for delivering high-quality services in a changing environment. Wide area network intelligence is a class of network intelligence in wide area network which covers the core and the edge of Internet. In this paper, we propose a system based on machine learning for wide area network intelligence. The whole system consists of a core machine for pre-training and many terminal machines to accomplish faster responses. Each machine is one of dual-hemisphere models which are made of left and right hemispheres. The left hemisphere is used to improve latency by terminal response and the right hemisphere is used to improve communication by data generation. In an application on multimedia service, the proposed model is superior to the latest deep feed forward neural network in the data center with respect to the accuracy, latency and communication. Evaluation shows scalable improvement with regard to the number of terminal machines. Evaluation also shows the cost of improvement is longer learning time.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E99-B No.11 pp.2263-2270
- Publication Date
- 2016/11/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2016NEP0003
- Type of Manuscript
- Special Section PAPER (Special Section on Deepening and Expanding of Information Network Science)
- Category
Authors
Yiqiang SHENG
Chinese Academy of Sciences
Jinlin WANG
Chinese Academy of Sciences
Yi LIAO
University of Chinese Academy of Sciences
Zhenyu ZHAO
University of Science and Technology of China
Keyword
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Yiqiang SHENG, Jinlin WANG, Yi LIAO, Zhenyu ZHAO, "A Machine Learning Model for Wide Area Network Intelligence with Application to Multimedia Service" in IEICE TRANSACTIONS on Communications,
vol. E99-B, no. 11, pp. 2263-2270, November 2016, doi: 10.1587/transcom.2016NEP0003.
Abstract: Network intelligence is a discipline that builds on the capabilities of network systems to act intelligently by the usage of network resources for delivering high-quality services in a changing environment. Wide area network intelligence is a class of network intelligence in wide area network which covers the core and the edge of Internet. In this paper, we propose a system based on machine learning for wide area network intelligence. The whole system consists of a core machine for pre-training and many terminal machines to accomplish faster responses. Each machine is one of dual-hemisphere models which are made of left and right hemispheres. The left hemisphere is used to improve latency by terminal response and the right hemisphere is used to improve communication by data generation. In an application on multimedia service, the proposed model is superior to the latest deep feed forward neural network in the data center with respect to the accuracy, latency and communication. Evaluation shows scalable improvement with regard to the number of terminal machines. Evaluation also shows the cost of improvement is longer learning time.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2016NEP0003/_p
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@ARTICLE{e99-b_11_2263,
author={Yiqiang SHENG, Jinlin WANG, Yi LIAO, Zhenyu ZHAO, },
journal={IEICE TRANSACTIONS on Communications},
title={A Machine Learning Model for Wide Area Network Intelligence with Application to Multimedia Service},
year={2016},
volume={E99-B},
number={11},
pages={2263-2270},
abstract={Network intelligence is a discipline that builds on the capabilities of network systems to act intelligently by the usage of network resources for delivering high-quality services in a changing environment. Wide area network intelligence is a class of network intelligence in wide area network which covers the core and the edge of Internet. In this paper, we propose a system based on machine learning for wide area network intelligence. The whole system consists of a core machine for pre-training and many terminal machines to accomplish faster responses. Each machine is one of dual-hemisphere models which are made of left and right hemispheres. The left hemisphere is used to improve latency by terminal response and the right hemisphere is used to improve communication by data generation. In an application on multimedia service, the proposed model is superior to the latest deep feed forward neural network in the data center with respect to the accuracy, latency and communication. Evaluation shows scalable improvement with regard to the number of terminal machines. Evaluation also shows the cost of improvement is longer learning time.},
keywords={},
doi={10.1587/transcom.2016NEP0003},
ISSN={1745-1345},
month={November},}
Copy
TY - JOUR
TI - A Machine Learning Model for Wide Area Network Intelligence with Application to Multimedia Service
T2 - IEICE TRANSACTIONS on Communications
SP - 2263
EP - 2270
AU - Yiqiang SHENG
AU - Jinlin WANG
AU - Yi LIAO
AU - Zhenyu ZHAO
PY - 2016
DO - 10.1587/transcom.2016NEP0003
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E99-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2016
AB - Network intelligence is a discipline that builds on the capabilities of network systems to act intelligently by the usage of network resources for delivering high-quality services in a changing environment. Wide area network intelligence is a class of network intelligence in wide area network which covers the core and the edge of Internet. In this paper, we propose a system based on machine learning for wide area network intelligence. The whole system consists of a core machine for pre-training and many terminal machines to accomplish faster responses. Each machine is one of dual-hemisphere models which are made of left and right hemispheres. The left hemisphere is used to improve latency by terminal response and the right hemisphere is used to improve communication by data generation. In an application on multimedia service, the proposed model is superior to the latest deep feed forward neural network in the data center with respect to the accuracy, latency and communication. Evaluation shows scalable improvement with regard to the number of terminal machines. Evaluation also shows the cost of improvement is longer learning time.
ER -
English
