ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2021, Vol. 58 ›› Issue (6): 1261-1274.doi: 10.7544/issn1000-1239.2021.20201073

所属专题: 2021云网融合专题

• 网络技术 • 上一篇    下一篇



  1. (首都师范大学信息工程学院 北京 100048) (
  • 出版日期: 2021-06-01
  • 基金资助: 

Design of an Intelligent Routing Algorithm to Reduce Routing Flap

Shao Tianzhu, Wang Xiaoliang, Chen Wenlong, Tang Xiaolan, Xu Min   

  1. (College of Information Engineering, Capital Normal University, Beijing 100048)
  • Online: 2021-06-01
  • Supported by: 
    This work was supported by the National Key Research and Development Program of China (2018YFB1800403), the National Natural Science Foundation of China (61872252), and Beijing Natural Science Foundation (4202012).

摘要: 近来,研究人员开始关注基于数据驱动的智能网络协议设计方法,以此取代依赖人类专家的传统协议设计方式.智能化路由技术也随之得到快速发展,但仍存在亟待解决的问题.研究了当前智能路由算法在路由更新过程中带来的大范围路由抖动以及转发效率下降问题.提出了一种路由抖动抑制的智能路由选择算法FSR(flap suppression routing),在追求全网链路负载均匀、转发资源高利用率的同时,寻求与现有路由策略最相似的更新方案,使得每个路由更新周期的路由抖动减小,缩短路由收敛时间,提升网络整体转发性能.实验表明:FSR算法能显著提升路由收敛速度,与对照算法相比提升约30%的网络吞吐量,同时降低路径长度和拥塞概率.

关键词: 路由算法, 机器学习, 深度神经网络, 流量规划, 网络振动

Abstract: Recently, researchers have begun to focus on data-driven network protocol design methods to replace traditional protocol design methods that rely on human experts. While the resulting intelligent routing technology is rapidly developing, there are also problems to be solved urgently. This paper studies the large-scale routing flapping caused by the current intelligent routing algorithm in the routing update process and the resulting decrease in forwarding efficiency of network. A smart routing algorithm, named FSR(flap suppression routing), for route flapping suppression is proposed. While pursuing the uniform link load of the entire network and making full use of the forwarding resources of the entire network, FSR seeks an update plan that is most similar to the existing routing strategies. This reduces routing flapping in each routing update cycle, reduces route convergence time, and improves overall network forwarding efficiency. Experiments have shown that FSR algorithm can significantly improve the routing convergence speed, increase the network throughput by about 30% compared with the control algorithms, and significantly reduce the path length and the probability of congestion.

Key words: routing algorithm, machine learning, deep neural network, traffic planning, routing flap