基于参数预测的智能多路径选择

Suyang Ju, Joseph B. Evans
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引用次数: 12

摘要

提出了一种基于参数预测的多路径选择方法。在无线网络中,不同带宽的链路会产生不同的端到端时延和丢包率特征。这意味着我们应该能够在给定端到端延迟和丢包率的情况下获得一些链路类型的知识。在这项工作中,我们使用神经网络机器学习方法来推断链接的类型。在预测了链路的类型之后,我们可以根据增量吞吐量的预测来选择路径,例如选择潜在增量吞吐量最大的路径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent Multi-Path Selection Based on Parameters Prediction
This paper provides a method for multi-path selection based on parameters prediction. In wireless networks, links with different bandwidths induce different end-to-end delay and the packet loss rate characteristics. It means that we should be able to gain some knowledge of the type of links given the end-to-end delay and the packet loss rate. In this work, we use a neural network machine learning method to infer the types of the links. After predicting the types of the links, we can choose the path based on the prediction of the incremental throughput, for example by choosing the path with the largest potential incremental throughput.
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