The paper addresses the challenge of adapting machine learning models for network traffic prediction to drastic changes in traffic patterns caused by network failures. It proposes a novel approach based on liquid neural networks (LNNs), which can adapt to changes in data patterns without the need for retraining.
The authors compare the performance of the LNN-based approach to a reference method based on incremental learning, which performs periodic retraining. They simulate dynamic network operations and failure scenarios to evaluate the predictive performance and adaptability of the two approaches.
The results show that the LNN-based approach outperforms incremental learning in situations where the shifts in traffic patterns are drastic, exhibiting lower root mean square error (RMSE) and faster convergence to reliable predictions. In contrast, incremental learning approaches perform better when the changes in traffic patterns are more moderate, especially when the retraining is performed less frequently (larger batch sizes).
The authors conclude that LNN-based adaptive learning can be particularly useful for network operators to quickly adapt to unexpected traffic patterns caused by network failures, while incremental learning may be preferred when the changes are more gradual. The findings provide valuable insights for selecting the appropriate machine learning approach for traffic prediction in the context of network failures.
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