Presentation | 2008-01-25 Congestion Control of TCP Using Neural Network Model Akihiro SHIOZU, Koki ABE, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We proposed TCP Salt, a congestion control scheme which predicts congestions by machine learning to obtain an optimum window size. It was found that the proposed method can utilize the bandwidth better than conventional methods. However, evaluation of the fairness with other connections is not sufficient. In this paper, we describe simulation results that TCP Salt can realize good fairness with other connections not only in the boost state but also in the congestion avoidance state. We also describe that using the previous value of congestion window as an input to machine learning improves the fairness. We further show that it is reasonable to use the time when the variation of RTT is zero within measurement accuracy as the tradition condition from the congestion avoidance state to the boost state. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | TCP / Congestion Control / Neural Network |
Paper # | NS2007-126 |
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Committee | NS |
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Conference Date | 2008/1/17(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
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) | Congestion Control of TCP Using Neural Network Model |
Sub Title (in English) | |
Keyword(1) | TCP |
Keyword(2) | Congestion Control |
Keyword(3) | Neural Network |
1st Author's Name | Akihiro SHIOZU |
1st Author's Affiliation | Department of Computer Science, The University of Electro-Communications() |
2nd Author's Name | Koki ABE |
2nd Author's Affiliation | Department of Computer Science, The University of Electro-Communications |
Date | 2008-01-25 |
Paper # | NS2007-126 |
Volume (vol) | vol.107 |
Number (no) | 443 |
Page | pp.pp.- |
#Pages | 6 |
Date of Issue |