Improving Internet Traffic Matrix Prediction via Time Series Clustering
This work addresses network optimization challenges for internet service providers by enhancing traffic prediction accuracy, though it is incremental as it builds on existing deep learning methods with clustering strategies.
The paper tackles the problem of improving internet traffic matrix prediction by addressing diverse temporal behaviors in traffic flows, resulting in up to 92% RMSE reduction for Abilene and 75% for GÉANT, with practical benefits like reduced maximum link utilization bias by up to 21%.
We present a novel framework that leverages time series clustering to improve internet traffic matrix (TM) prediction using deep learning (DL) models. Traffic flows within a TM often exhibit diverse temporal behaviors, which can hinder prediction accuracy when training a single model across all flows. To address this, we propose two clustering strategies, source clustering and histogram clustering, that group flows with similar temporal patterns prior to model training. Clustering creates more homogeneous data subsets, enabling models to capture underlying patterns more effectively and generalize better than global prediction approaches that fit a single model to the entire TM. Compared to existing TM prediction methods, our method reduces RMSE by up to 92\% for Abilene and 75\% for GÉANT. In routing scenarios, our clustered predictions also reduce maximum link utilization (MLU) bias by 18\% and 21\%, respectively, demonstrating the practical benefits of clustering when TMs are used for network optimization.