Intelligent traffic engineering methods in SDN: Performance analysis and prospects for application
DOI:
https://doi.org/10.18372/2073-4751.84.20897Keywords:
artificial intelligence, enterprise networks, SDN, Smart Grids, transport networks, UAV networksAbstract
Software-defined networks (SDN) create favorable conditions for deploying intelligent traffic management methods thanks to centralized control and global visibility of the network state. At the same time, traditional load balancing algorithms cannot effectively adapt to dynamic traffic changes and complex topologies. This paper analyzes the effectiveness of the intelligent load-balancing method LBBNN, based on an artificial neural network that considers the global network state when selecting a route. The evaluation was carried out in a Mininet environment on sparsely connected, fully connected, and de Bruijn topologies. The results demonstrate reduced delays and packet loss and a more uniform load distribution compared to Round Robin, Dynamic Load Balancing (DLB), and modified ECMP. The neural-network approach improves quality of service (QoS) and enables more efficient use of network resources in dynamic SDN environments.
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Artem Volokyta, Alla Kogan, Oleksii Cherevatenko, Dmytro Korenko, Dmytro Oboznyi, Yurii Kulakov, "Traffic Engineering with Specified Quality of Service Parameters in Software-defined Networks", International Journal of Computer Network and Information Security(IJCNIS), Vol.16, No.5, pp.1-13, 2024. DOI:10.5815/ijcnis.2024.05.01
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