Communication network load balance max-flow routing method

Abstract

The present invention belongs to the technical field of wireless communication, and discloses a communication network load balance max-flow routing method. The method comprises the steps of: performing layering of a communication network topological graph to obtain a layered network, classifying the a link and a path according to the layered network, and performing node height marking of network nodes; employing a mode of balance promotion through saturation pre-flow to allow the network to reach the max flow; and performing balance shunt regulation. On the basis of the max flow, an idle equivalent path is searched to perform shunt regulation to allow more idle links to participate in shunt so as to further improve the network load balance performances, namely, the flow is more evenly distributed on the links to improve the reasonability of the network resource utilization.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a communication network load balancing maximum flow routing method. Background technique [0002] As a platform for information transmission, the network is an essential part of communication. With the rapid growth of communication services, it is necessary to increase the throughput of the communication network so that it can meet the increasing communication demands. In order to solve the problem of maximizing network throughput, the maximum flow method is generally used in routing algorithms to analyze and solve. The maximum flow problem refers to the decision-making and control of the routing of information flow, data flow, etc. flowing through the network in the case of limited link capacity, so as to maximize the overall flow of the network. Obtaining the maximum network flow has important guiding significance for the maximum utilization of comm...

AI Technical Summary

Problems solved by technology

[0003] There have been many achievements in the network maximum flow routing algorithm, but the goals of these achievements are mostly focused on reducing the complexity of the algorithm and improving the efficiency of the algorithm, and the consideration of load balancing is rarely involved.

When the 2F (Ford-Fulkerson) algorithm selects the maximum flow route, the selection of the path with available capacity, that is, the augmented path is random, which means that there are many feasible solutions for the maximum flow route obtained from the same network topology map , that is, the maximum flow network distribution map obtained by the 2F algorithm is inconsistent each time, which will lead to the randomness of the traffic distribution on the link, making the load balancing performance unstable, resulting in poor load balancing performance

The Shortest Augmented Path (SAP) algorithm selects the shortest augmented path with the least number of edges each time for traffic augmentation, which may make the traffic too concentrated on the path composed of shorter links, while the longer path Links may be idle for a long time, causing local links to be too busy. This unreasonable utilization of resources will lead to poor load balancing performance

Because the general pre-flow push algorithm will give priority to a single intermediate node that is closer to the destination node for traffic push each time, it may also cause the problem that the traffic is too concentrated on the path composed of shorter links, resulting in partial link Too busy, resulting in poor load balancing performance

[0004] These existing network maximum flow routing algorithms can obtain routing decision results that satisfy the network maximum flow, but there are unbalanced link loads in the routing formed by the decision, which will lead to insufficient utilization of resources in the network

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