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  1. 研究報告
  2. インターネットと運用技術(IOT)
  3. 2018
  4. 2018-IOT-040

Towards Self-Optimizing Network: Applying Deep Learning to Network Traffic Categorization and Identification in the Context of Application-Aware Network

https://ipsj.ixsq.nii.ac.jp/records/186244
https://ipsj.ixsq.nii.ac.jp/records/186244
70c9f882-fcb2-4e1b-bc9c-a74cc8ea4dcd
名前 / ファイル ライセンス アクション
IPSJ-IOT18040006.pdf IPSJ-IOT18040006.pdf (1.4 MB)
Copyright (c) 2018 by the Information Processing Society of Japan
オープンアクセス
Item type SIG Technical Reports(1)
公開日 2018-02-26
タイトル
タイトル Towards Self-Optimizing Network: Applying Deep Learning to Network Traffic Categorization and Identification in the Context of Application-Aware Network
タイトル
言語 en
タイトル Towards Self-Optimizing Network: Applying Deep Learning to Network Traffic Categorization and Identification in the Context of Application-Aware Network
言語
言語 eng
キーワード
主題Scheme Other
主題 動的構成技術
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_18gh
資源タイプ technical report
著者所属
Nara Institute of Science and Technology
著者所属
Osaka University
著者所属
Nara Institute of Science and Technology
著者所属
Nara Institute of Science and Technology
著者所属(英)
en
Nara Institute of Science and Technology
著者所属(英)
en
Osaka University
著者所属(英)
en
Nara Institute of Science and Technology
著者所属(英)
en
Nara Institute of Science and Technology
著者名 Pongsakorn, U-Chupala

× Pongsakorn, U-Chupala

Pongsakorn, U-Chupala

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Yasuhiro, Watashiba

× Yasuhiro, Watashiba

Yasuhiro, Watashiba

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Kohei, Ichikawa

× Kohei, Ichikawa

Kohei, Ichikawa

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Hajimu, Iida

× Hajimu, Iida

Hajimu, Iida

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著者名(英) Pongsakorn, U-Chupala

× Pongsakorn, U-Chupala

en Pongsakorn, U-Chupala

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Yasuhiro, Watashiba

× Yasuhiro, Watashiba

en Yasuhiro, Watashiba

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Kohei, Ichikawa

× Kohei, Ichikawa

en Kohei, Ichikawa

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Hajimu, Iida

× Hajimu, Iida

en Hajimu, Iida

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論文抄録
内容記述タイプ Other
内容記述 The application-aware routing is a network routing technology optimized for a network with an inconsistent link performance, a problem which is common for a multi-institution research and academic network. Using the application-aware routing, an application-aware network routes each flow independently via the optimal path corresponding to the identified application characteristic. This technology enables the creation of a self-optimizing network. However, an automatic network flow categorization and identification system is required. In the scope of this work, network flow categorization is defined as the process of generating a meaningful classification whereas network flow identification is defined as identifying which class a network flow belongs to. These are challenging problems with various applicabilities. We present a deep learning approach to network flow categorization and identification problems. Deep learning provides several advantages over existing solutions in the context of the application-aware network. According to our experiments, a 3-layer stacked denoising autoencoder trained with CAIDA Internet traffic dataset produces the most meaningful classification and a useful class identifier (classifier). This deep neural network (DNN) model generates three-classes classification: a bandwidth-bound pattern, a latency-bound pattern, and an irregular pattern. A design of a highly scalable implementation of a self-optimizing network using a DNN model is also presented with justification for each design decision. Our findings suggest that a deep learning approach to network flow categorization and identification problems in the context of the application-aware network and the self-optimizing network are promising.
論文抄録(英)
内容記述タイプ Other
内容記述 The application-aware routing is a network routing technology optimized for a network with an inconsistent link performance, a problem which is common for a multi-institution research and academic network. Using the application-aware routing, an application-aware network routes each flow independently via the optimal path corresponding to the identified application characteristic. This technology enables the creation of a self-optimizing network. However, an automatic network flow categorization and identification system is required. In the scope of this work, network flow categorization is defined as the process of generating a meaningful classification whereas network flow identification is defined as identifying which class a network flow belongs to. These are challenging problems with various applicabilities. We present a deep learning approach to network flow categorization and identification problems. Deep learning provides several advantages over existing solutions in the context of the application-aware network. According to our experiments, a 3-layer stacked denoising autoencoder trained with CAIDA Internet traffic dataset produces the most meaningful classification and a useful class identifier (classifier). This deep neural network (DNN) model generates three-classes classification: a bandwidth-bound pattern, a latency-bound pattern, and an irregular pattern. A design of a highly scalable implementation of a self-optimizing network using a DNN model is also presented with justification for each design decision. Our findings suggest that a deep learning approach to network flow categorization and identification problems in the context of the application-aware network and the self-optimizing network are promising.
書誌レコードID
収録物識別子タイプ NCID
収録物識別子 AA12326962
書誌情報 研究報告インターネットと運用技術(IOT)

巻 2018-IOT-40, 号 6, p. 1-6, 発行日 2018-02-26
ISSN
収録物識別子タイプ ISSN
収録物識別子 2188-8787
Notice
SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
出版者
言語 ja
出版者 情報処理学会
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