Improving Slow Transfer Predictions: Generative Methods Compared
This is an incremental improvement for network administrators monitoring scientific computing transfers.
The paper tackled the class imbalance problem in predicting slow data transfers for scientific computing networks by comparing various augmentation strategies, finding that even advanced generative techniques like CTGAN didn't significantly outperform simple stratified sampling as imbalance ratios increased.
Monitoring data transfer performance is a crucial task in scientific computing networks. By predicting performance early in the communication phase, potentially sluggish transfers can be identified and selectively monitored, optimizing network usage and overall performance. A key bottleneck to improving the predictive power of machine learning (ML) models in this context is the issue of class imbalance. This project focuses on addressing the class imbalance problem to enhance the accuracy of performance predictions. In this study, we analyze and compare various augmentation strategies, including traditional oversampling methods and generative techniques. Additionally, we adjust the class imbalance ratios in training datasets to evaluate their impact on model performance. While augmentation may improve performance, as the imbalance ratio increases, the performance does not significantly improve. We conclude that even the most advanced technique, such as CTGAN, does not significantly improve over simple stratified sampling.