Estimating TCP throughput: A neural network approach

被引：0

作者：

Chen, Hualiang ^{[1
]}

Liu, Zhongxin ^{[1
]}

Chen, Zengqiang ^{[1
]}

Yuan, Zhuzhi ^{[1
]}

机构：

[1] Nankai Univ, Coll Informat Tech Sci, Dept Automat, Tianjin 300071, Peoples R China

来源：

WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS | 2006年

关键词：

TCP throughput; congestion control; neural network; model;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We address a neural network approach for modeling the behavior of TCP congestion control. After trained with typical data samples, a three-layer (3-10-1) neural network model with fixed weights has been tested over a wide range of network conditions. In contrast to the equation models, our model can better associate the TCP factors, i.e. round trip time (RTT), retransmission timeout (RTO) and the loss event rate, with the throughput. Therefore, it can more accurately estimate the TCP throughput. As the estimation is done through the fixed neural model, the computational complexity is small, so it can be used for real-time online computing.

引用

页码：2850 / +

页数：2

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