Multipath Routing Method Based on Reinforcement Learning and Transfer Learning

Abstract

The present invention proposes a multi-path routing method based on reinforcement learning and transfer learning, which is used to solve the technical problem of poor load balance of equivalent paths in a network environment with less traffic data existing in the prior art, and the implementation steps are as follows : Construct the real network Z and the experimental network G consistent with the topological structure of Z; establish a two-dimensional array H; construct a multi-path routing model based on reinforcement learning; initialize the traffic matrix DM and the equivalent path traffic ratio matrix PM; The multi-path routing model based on reinforcement learning is iteratively trained; the weight parameters of the global neural network in the routing decision model trained in the experimental network G are transferred to the real network Z based on the transfer learning method; The initialized global neural network performs adaptive training to obtain multi-path routing results that conform to the characteristics of the real network environment. The present invention can be used in scenarios such as a data center network.

Description

technical field [0001] The invention belongs to the technical field of computer networks and relates to a multi-path routing method based on reinforcement learning and migration learning, which can be used in fields such as data center networks. Background technique [0002] For the network, the routing decision stipulates how the data flow reaches another node from a specified node in the network. The routing decision can schedule the traffic, so it determines the load balance of different transmission paths in the network, and measures the network load balance status. The value of is the difference in bandwidth utilization of all equivalent paths between two communication pairs in the network. The smaller the difference, the better the load balance of the equivalent path. Routing decision-making can be divided into traditional routing decision-making methods and routing decision-making methods based on reinforcement learning. The traditional routing decision-making algorit...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to address the shortcomings of the above-mentioned prior art, and propose a multi-path routing method based on reinforcement learning and transfer learning, which is used to solve the equivalent path load in the network environment with less traffic data existing in the prior art Poorly balanced technical issues

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