Prophet: Traffic Engineering-Centric Traffic Matrix Prediction
Yuntian Zhang, Ning Han, Tengteng Zhu, Junjie Zhang, Minghao Ye, Songshi Dou, Zehua Guo
Abstract
Traffic Matrix (TM), which records traffic volumes among network nodes, is important for network operation and management. Due to cost and operation issues, TMs cannot be directly measured and collected in real time. Therefore, many studies work on predicting future TMs based on historical TMs. However, existing works are usually accuracy-centric prediction solutions that mainly focus on improving predicting accuracy of flows’ sizes (i.e., values of elements in TMs) without considering the practical application of TMs. In this paper, we propose a novel TM prediction solution called Prophet for Traffic Engineering (TE), a typical application for TMs which takes TMs as input to optimize routing. We identify that the critical property (i.e., ratio among elements) in a TM plays an important role in TE’s performance. Based on this analysis, we adopt the matrix normalization to maintain the critical property in TMs and customize a TE-centric angle loss function to introduce scale invariance of TMs for capturing the overall relationship error. Different from the element-wise Mean Squared Error (MSE) loss function in accuracy-centric prediction solutions, our proposed TE-centric angle loss function has a clear geometric interpretation, which confines the angle between predicted TM and real TM to zero. Simulation results show that the predicted TMs from Prophet can improve the performance of link-level TE and path-level TE by up to 45.4% and 52.8%, respectively, compared to existing solutions.