Presentation | 2013-04-18 A Low-Quality Link Detection Scheme Using Compressed Sensing-Based Network Tomography Kazushi TAKEMOTO, Takahiro MATSUDA, Tetsuya TAKINE, |
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Abstract(in English) | Network tomography is an inference technique for internal network characteristics from end-to-end measurements. In this article, we study network tomography based on compressed sensing, which is an emerging theory in signal/image processing for acquiring sparse vectors. By applying compressed sensing to network tomography, we can efficiently detect low-quality links with the small number of measurements. We propose an online network tomography scheme for detecting links with high packet loss. In the proposed scheme, two measurement nodes establish measurement paths between them, and measure packet loss rates on these paths within every measurement window. When high packet loss rates are observed on some paths, we identify low-quality links by using compressed sensing-based network tomography. The performance of the proposed scheme is evaluated with simulation experiments. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Network Tomography / Compressed Sensing / Sparse Vector |
Paper # | NS2013-1 |
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Committee | NS |
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Conference Date | 2013/4/11(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Network Systems(NS) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | A Low-Quality Link Detection Scheme Using Compressed Sensing-Based Network Tomography |
Sub Title (in English) | |
Keyword(1) | Network Tomography |
Keyword(2) | Compressed Sensing |
Keyword(3) | Sparse Vector |
1st Author's Name | Kazushi TAKEMOTO |
1st Author's Affiliation | Graduate School of Engineering, Osaka University() |
2nd Author's Name | Takahiro MATSUDA |
2nd Author's Affiliation | Graduate School of Engineering, Osaka University |
3rd Author's Name | Tetsuya TAKINE |
3rd Author's Affiliation | Graduate School of Engineering, Osaka University |
Date | 2013-04-18 |
Paper # | NS2013-1 |
Volume (vol) | vol.113 |
Number (no) | 4 |
Page | pp.pp.- |
#Pages | 6 |
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