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.
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.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2013年第7期1290-1296,共7页
Acta Electronica Sinica
基金
国家863高技术研究发展计划(No.2011AA010701)
中央高校基本科研业务费专项资金(No.2011JBM018)
国家自然科学基金(No.60972010
No.60903150
No.61100219)
关键词
自学习
多路径并行传输
最优化
选路算法
autonomous learning
concurrent multipath transfer（CMT）
optimization
path selection
