書誌事項
- タイトル別名
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- Improving Bandwidth Utilization and Fairness between TCP Flows based on a Machine-learning Approach
- タイイキ ノ ユウコウ リヨウ ト コウヘイセイ オ コウリョ シタ キカイ ガクシュウガタ TCPフクソウセイギョ
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抄録
TCP, a current de facto standard transport-layer protocol of the Internet, cannot fully utilize the available bandwidth. Fairness between TCP flows is another important measure of TCP performance. We proposed a method for predicting the optimal size of the congestion window to avoid network congestion by using a machine learning approach. In this paper, based on the machine learning approach, we further improve the congestion algorithm with respect to utilization of the available bandwidth and fairness between TCP flows. The improvement includes bringing a size of the congestion windows closer to the optimum value, realizing fairness against congestion algorithms that aggressively use bandwidth, and adapting to the network where the available bandwidth abruptly changes. The proposed method is evaluated with respect to utilization of bandwidth and fairness between TCP flows including flows aggressively using bandwidth by simulation using NS-2.
収録刊行物
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- 電気学会論文誌Ｃ（電子・情報・システム部門誌）
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電気学会論文誌Ｃ（電子・情報・システム部門誌） 133 (6), 1259-1268, 2013
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390282679586316672
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- NII論文ID
- 10031175310
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- NII書誌ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL書誌ID
- 024702012
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可