ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 12. Vol. 29. 2023

DOI: 10.17587/it.29.622-629

D. A. Perepelkin, Dr. Sc. (Tech.), Dean of the Faculty of Computer Engineering, M. A. Ivanchikova, Dr. Sc. (Tech.), Associate Professor of the CAD Department, V. T. Nguyen, Postgraduate Student,
Ryazan State Radio Engineering University, Ryazan, Russian Federation

Neural Network Multipath Routing in Software Defined Networks Based on Genetic Algorithm

Currently, a wide demand for the implementation and use of various cloud solutions is a modern trend and the driving force behind the development of network technologies. The growth of cloud application services delivered through data centers with varying network traffic needs demonstrates the limitations of traditional routing and load balancing methods. The combination of the advantages of software defined networks (SDN) technology and artificial intelligence (AI) methods ensures efficient management and operation of computer network resources. The paper proposes an approach to neural network multipath routing in SDN based on a genetic algorithm. The architecture and model of an artificial neural network has been developed to solve the problem of multipath routing in the SDN, which is able to predict the shortest paths based on the metrics of communication links. To optimize the hyperparameters of the neural network model, it is proposed to use a modified genetic algorithm. A visual software system SDNLoadBalancer has been developed and an experimental SDN topology has been designed, which makes it possible to study in detail the processes of neural network multipath routing in SDN based on the proposed approach. The obtained results show that the proposed neural network model has the ability to predict routes with high accuracy in real time, which makes it possible to implement various load balancing schemes in order to increase performance of SDN.
Keywords: software defined networks, neural network routing, multipath routing, recurrent neural networks, genetic algorithm

P. 622-629

Acknowledgements: The study was supported by the Grant of the Russian Science Foundation and Government of Ryazan Region, project No. 22-21-20093.

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