Deep learning-based SDN network traffic advantageous monitoring node dynamic selection system and dynamic selection method thereof

The invention discloses a deep learning-based SDN network traffic advantageous monitoring node dynamic selection system and a dynamic selection method thereof. A SDN control plane comprises a forwarding calculation module, a node updating module and a path prediction module. A SDN data layer compris...

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Hauptverfasser: ZHAO HUI, WANG LIANGMIN, YAO YIRU, FENG XIA, HAN ZHIGENG, CHEN XIANGYI, SHENTU HAO
Format: Patent
Sprache:chi ; eng
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Zusammenfassung:The invention discloses a deep learning-based SDN network traffic advantageous monitoring node dynamic selection system and a dynamic selection method thereof. A SDN control plane comprises a forwarding calculation module, a node updating module and a path prediction module. A SDN data layer comprises a network resource module. The dynamic selection method includes an advantageous monitoring node pre-screening stage and an advantageous monitoring node dynamic updating stage. The advantageous monitoring node pre-screening stage only operates when the system is cold boosted. The advantageous monitoring node dynamic updating stage operates adaptively in a closed-loop self-feedback mode after the system startup is completed. The dynamic selection system, in view of selecting a monitoring node, preferentially selects a switch with the densest traffic traversal as a traffic monitoring node, and achieves a purpose of increasing traffic collection non-redundancy rate and reducing the traffic monitoring overhead while