Item type |
SIG Technical Reports(1) |
公開日 |
2018-02-26 |
タイトル |
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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 |
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言語 |
en |
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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 |
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言語 |
eng |
キーワード |
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主題Scheme |
Other |
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主題 |
動的構成技術 |
資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_18gh |
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資源タイプ |
technical report |
著者所属 |
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Nara Institute of Science and Technology |
著者所属 |
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Osaka University |
著者所属 |
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Nara Institute of Science and Technology |
著者所属 |
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Nara Institute of Science and Technology |
著者所属(英) |
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en |
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Nara Institute of Science and Technology |
著者所属(英) |
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en |
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Osaka University |
著者所属(英) |
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en |
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Nara Institute of Science and Technology |
著者所属(英) |
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en |
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Nara Institute of Science and Technology |
著者名 |
Pongsakorn, U-Chupala
Yasuhiro, Watashiba
Kohei, Ichikawa
Hajimu, Iida
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著者名(英) |
Pongsakorn, U-Chupala
Yasuhiro, Watashiba
Kohei, Ichikawa
Hajimu, Iida
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論文抄録 |
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内容記述タイプ |
Other |
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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) |
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内容記述タイプ |
Other |
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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 |
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収録物識別子タイプ |
NCID |
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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
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ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2188-8787 |
Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |