Dynamic Adaptation of In-Band Network Monitoring via Meta Learning

Wide Area Network (WAN) management faces significant complexity in characterizing heterogeneous traffic from diverse services, which complicates unified feature extraction for operational tasks like anomaly detection. Key challenges persist in difficulty of creating generic feature sets across diverse traffic patterns and insufficient historical data for machine learning (ML) generalization. Meta-learning algorithms can be applied to learn dynamic feature sets to improve model generalizability on a limited number of data records. However, prior research focuses on algorithm design, with a lack of study on its application to in-band network monitoring. This work proposes a workflow to adaptively collect new feature sets and promptly learn from them. An adaptive in-band feature selection and extraction method is proposed for programmable switch. Meta learning algorithm is introduced for prompt decision in controller based on few-shot records. Evaluation results have shown that it outperforms prior methods in accuracy and inference time on public datasets.