帯域の有効利用と公平性を考慮した機械学習型TCP輻輳制御

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2013/06/01

タイトル(英語): Improving Bandwidth Utilization and Fairness between TCP Flows based on a Machine-learning Approach

著者名: 塩津　晃明（電気通信大学 情報工学科／電気通信大学 情報基盤センター），矢崎　俊志（日本電信電話（株）NTTネットワーク基盤技術研究所），阿部　公輝（電気通信大学 情報工学科）

著者名(英語): Akihiro Shiozu (Department of Computer Science, The University of Electro-Communications/Information Technology Center, The University of Electro-Communications), Syunji Yazaki (NTT Network Technology Laboratories, NTT Corporation), Koki Abe (Department of Computer Science, The University of Electro-Communications)

キーワード: TCP，輻輳制御，機械学習，帯域の有効利用，公平性　　TCP，congestion control，machine learning，bandwidth utilization，fairness

要約(英語): 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.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.133 No.6 （2013） 特集：非線形システムのモデル化・制御理論・応用の最前線

本誌掲載ページ: 1259-1268 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/133/6/133_1259/_article/-char/ja/