A Machine Learning Based Approach for Available Bandwidth Estimation
Date Issued
2006
Date
2006
Author(s)
Wang, Bo-Chun
DOI
en-US
Abstract
The available bandwidth of a path is determined by the link with the minimum unused bandwidth. The estimation of available bandwidth is useful for many applications, such as route selection, server selection, admission control, and etc. In recent years, there are many tools that have been proposed to improve the estimation of available bandwidth. The two most popular models are the probe gap model and the probe rate model. Since tools based on these two models have become mature, we propose a tool that combines statistical methods with these two models.
The basic idea of our tool is that we can collect many different data under different available bandwidth. After collecting enough information, we use statistical methods to analyze these data and use results to estimate available bandwidth. In our simulation, we use two methods to collect data, including dispersions, packet loss rate, and etc. Then we use SVM to train these attributes and estimate available bandwidth. In this paper, we describe our tool in detail, and show some results to illustrate the tool’s accuracy.
Subjects
可用頻寬
機器學習
測量
available bandwidth
machine learning
estimation
Type
thesis
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