Predicting future traffic using Hidden Markov Models

Zhitang Chen, Jiayao Wen, Yanhui Geng

Abstract: Network traffic volume estimation and prediction is an important research topic that attracts persistent attention from the networking community and the machine learning community. Although there has been extensive work on estimating or predicting the traffic matrix using time series models, low rank matrix decomposition et. al, to the best of our knowledge, there is few work investigating the problem whether we are able to estimate and predict the traffic volume based on some statistics of the traffic which are much less costly to collect, for example, the flow counts. In this paper, we propose to model the relationship between the traffic volume and simple statistics about flows using a Hidden Markov Model based on which we can avoid direct measurement of the traffic volume but instead we estimate and predict the hidden traffic volume based on those simple flow statistics which are collected by some sketch techniques. We demonstrate the feasibility and effectiveness of our proposed method using some semi-simulation and real data experimental results.