Dynamic load balancing model for data centres based on bidirectional network edge detection algorithm

To resolve link congestion and QoS deterioration caused by elephant flow conflicts in data centre networks under conventional multipath routing, this study proposes an innovative dynamic load balancing model.It combines software-defined networking (SDN) for centralised control with a Transformer-BiLSTM-DNN deep learning framework: the transformer encoder grasps global spatial correlations among traffic flows, BiLSTM retrieves intricate temporal variations, and DNN achieves deep feature integration for precise load state categorisation, enabling refined proactive traffic regulation.Evaluated on Google Borg Cluster Trace v2, the model excels in key metrics like load balancing degree, job completion time and resource utilisation, outperforming traditional baselines significantly.Ablation tests verify the essentiality of each component, providing a sturdy intelligent approach to enhance data centre network efficiency and dependability.