融合注意力机制和节点嵌入的FlexE资源分配研究OA

To address the issue of low accuracy in predicting network traffic for FlexE clients,an attention mechanism that can better capture local contextual information of network traffic sequences is designed.The node embedding method was introduced into the prediction model to extract personalized features of different FlexE clients.To reduce the significant increase in parameter size caused by node embedding,a matrix decomposition method was proposed.We allocated the FlexE calendar slot based on the prediction results and compared the performance of different resource allocation method.The experimental results demonstrate the effectiveness of the proposed method.