Flow-by-flow traffic matrix prediction methods: Achieving accurate, adaptable, low cost results

Weiping Zheng, Yiyong Li, Minli Hong, Xiaomao Fan, Gansen Zhao

Abstract: Traffic matrix (TM) prediction methods aim to accurately and efficiently predict future network traffic demands by using previous traffic matrices. These methods are critical for network operation and management. The recent research focuses on designing complex models to extract spatiotemporal correlations from TMs and network topologies using traffic volume datasets that consist of all the origin–destination (OD) flows sampled from the network. These entire-matrix models are confined to a specific network topology and are difficult to apply to other networks. To solve such problems, we investigate a flow-by-flow prediction method in this paper which utilizes the intra-flow temporal correlations. State-of-the-art results have been achieved by our method on Abilene and GÉANT datasets. Using only 10% of the OD flows as the training samples, the flow-by-flow models can outperform the previously achieved state-of-the-art prediction performance. This small sample greatly reduces the end-to-end traffic monitoring costs. In addition, the flow-by-flow models can achieve outstanding cross-dataset evaluation performance. We suggest that leveraging a large external dataset with a flow-by-flow method can be a better choice when the measurement environment is difficult on the target network. The source code for our prediction method is publicly available at https://github.com/FreeeBird/Flow-By-Flow-Prediction.