Presentation 2017-12-21
Congestion Analysis of Variation in Network Traffic by DeepLearning
Haruka Osanai, Masato Oguchi,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) When a large scale disaster happens, it is important that we can communicate with other people by phone or e- mail. However, these communication tools are hardly able to be used in such cases due to network congestion. Therefore, we must detect and deal with communication failures efficiently. Another study proposed an automatic traffic control system based on SNS. In order to detect congestion state, this study proposes a way to analyze a change of network traffic and study congestion state. Next, we show that it can predict a transition of network traffic.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) DeepLearning / Congestion / Network Traffic / Variation Prediction
Paper # MoNA2017-29
Date of Issue 2017-12-14 (MoNA)

Conference Information
Committee MoNA
Conference Date 2017/12/21(1days)
Place (in Japanese) (See Japanese page)
Place (in English) Ochanomizu University
Topics (in Japanese) (See Japanese page)
Topics (in English) Cloud computing, Big data, Wireless network, etc.
Chair Ryoichi Shinkuma(Kyoto Univ.)
Vice Chair Shigeaki Tagashira(Kansai Univ.) / Gen Kitagata(Tohoku Univ.)
Secretary Shigeaki Tagashira(Kyushu Univ.) / Gen Kitagata(NTT)
Assistant Takayuki Nishio(Kyoto Univ.) / Takato Saito(NTT)

Paper Information
Registration To Technical Committee on Mobile Network and Applications
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Congestion Analysis of Variation in Network Traffic by DeepLearning
Sub Title (in English)
Keyword(1) DeepLearning
Keyword(2) Congestion
Keyword(3) Network Traffic
Keyword(4) Variation Prediction
1st Author's Name Haruka Osanai
1st Author's Affiliation Ochanomizu University(Ochanomizu Univ.)
2nd Author's Name Masato Oguchi
2nd Author's Affiliation Ochanomizu University(Ochanomizu Univ.)
Date 2017-12-21
Paper # MoNA2017-29
Volume (vol) vol.117
Number (no) MoNA-371
Page pp.pp.11-14(MoNA),
#Pages 4
Date of Issue 2017-12-14 (MoNA)