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