Optimization method of carrier network

Abstract

The invention relates to an optimization method of a carrier network, which comprises the following steps: randomly generating an IP network structure fully connected among nodes, and setting a vector matrix formed by metric value of each link of the IP network as an initial population; according to the metric values in the vector matrix, the IP network structure and the unit price parameter of the network cost, obtaining a total investment expression of the IP network and a transmission network; combining the total investment expression with global restriction conditions to obtain an individual fitness expression; carrying out iteration based on the individual fitness expression; if an exit condition is not satisfied in each iteration, carrying out interlace operation and mutation operation on the population obtained in the last iteration; and if an exit condition is satisfied in each iteration, outputting the optimization result. The invention optimizes the total investment expression of the carrier network with the global restriction conditions by a genetic algorithm, obtains the optimization result by considering the difference between the two networks, and coordinates the performance and the investment benefit, thereby improving the overall performance and the resource utilization rate of a core transmission network and reducing the overall investment of the network.

Description

Technical field [0001] The present invention relates to network optimization technology, in particular to a method for optimizing an information service bearing network. Background technique [0002] As a unified information service bearer network in the future, the core transport network (IP network and optical transport network) is facing service bearer requirements with higher bandwidth, more complex traffic flow, and higher quality requirements. It also faces a more reasonable use of network resources and improvement. Network efficiency, thereby reducing the pressure on network investment costs. [0003] There are some mature heuristic algorithms for existing network optimization, such as simulated annealing algorithm, artificial neural network, tabu search, evolutionary algorithm, ant colony algorithm, genetic algorithm, etc. However, the network optimization mentioned here is generally applied to wireless The optimization of network, urban bus network, MPLS network QOS, logi...

AI Technical Summary

Problems solved by technology

[0006] One is that at the beginning of the study of the network structure of the IP network, factors such as the network structure, transmission distance, and construction cost of the underlying optical transport network were not considered, and only the most economical IP network construction was considered, resulting in a seemingly short path at the IP layer. Layer routes are longer and occupy more transmission resources

Often, only business requirements are considered at the IP layer. After the network construction is completed, the underlying construction is carried out at the transmission layer. The transmission layer builds as many transmission channels as the demand of the IP layer has, which is not optimal in terms of overall resource considerations;

[0007] Second, due to factors such as the structure of the optical cable network and the quality of the optical cable, sometimes the construction of the WDM network cannot fully meet the needs of the IP network.

[0008] Therefore, although the current network optimization methods are relatively simple and mature, due to the above two problems, the overall investment in the network remains high, and the performance and resource utilization of the network cannot be improved, which seriously restricts the maximum capacity of the network itself. play

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