Autonomous Learning Path Selection Scheme for Concurrent Multipath Transfer

LIU Chang; SONG Fei; YAN Huan; ZHANG Si-dong

doi:10.3969/j.issn.0372-2112.2013.07.008

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Autonomous Learning Path Selection Scheme for Concurrent Multipath Transfer

更新时间：2025-07-16

- Autonomous Learning Path Selection Scheme for Concurrent Multipath Transfer
- Acta Electronica Sinica Vol. 41, Issue 7, Pages: 1290-1296(2013)
- 作者机构：
  
  北京交通大学下一代互联网互联设备国家工程实验室,北京,100044
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2013.07.008
  CLC： TN915
- Published：2013
- 稿件说明：
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LIU Chang, SONG Fei, YAN Huan, et al. Autonomous Learning Path Selection Scheme for Concurrent Multipath Transfer[J]. Acta Electronica Sinica, 2013, 41(7): 1290-1296.
DOI：

LIU Chang, SONG Fei, YAN Huan, et al. Autonomous Learning Path Selection Scheme for Concurrent Multipath Transfer[J]. Acta Electronica Sinica, 2013, 41(7): 1290-1296. DOI： 10.3969/j.issn.0372-2112.2013.07.008.

摘要

本文针对多路径并行传输提出了一种基于自学习思想的路径选择算法.这种自学习机制首先根据具体服务提出的需求

如吞吐量、延时、丢包等

综合定义一个用于计算服务体验的目标函数

再将路径选择算法中的一些参数设定为可学参数.在传输过程中

系统通过分析不同可学习参数与目标函数值之间的对应变化关系

逐渐进行自我学习

得到最佳的可学参数配置

从而获得最优化的服务体验.实验测试结果表明了自学习选路算法的可行性、收敛性和稳定性

并且证明了该算法能够根据网络状态的实时变化

通过自学习机制自行调整路径分配并得到最佳的目标函数值.

Abstract

In this paper

we propose a path selection scheme based on the idea of autonomous learning for concurrent multipath transfer (CMT) in path selection.Firstly

this autonomous learning scheme integrates the requirements of a specific service

such as throughput

delay and loss rate

to define the optimization object function.Secondly

this scheme sets several parameters of path selection as the learning parameters.During the transmission

through analyzing the relationship between different values of learning parameters and the actual measured values of object function

the system performs its autonomous learning

and gradually gets the best value combination of learning parameters.Experimentations show the feasibility

convergence and stability of the scheme

and also demonstrate the scheme could adjust the learning parameters by itself to adapt the changing network condition and gain the best value of object function.

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