Machine learning in eigensubspace for network path identification and flow forecast

This paper emphasizes the joint time-frequency interpretation of eigensubspace representation for network statistics as features for identification and tracking traffic flows based on the link level activity. Eigencoefficients (frequency domain feature) and eigenvector components (time domain feature) are jointly utilized to quantify their combined significance on the representation of each link data (each component of the link traffic vector) in the eigensubspace. The joint time-frequency method is employed to analyze the traffic data obtained from the Internet2 network. It is shown that the analysis with link-level resolution brings advantages for network traffic engineering applications. A machine learning method is investigated to identify network paths using eigenanalysis of link statistics as the feature set. The merit of the method is validated by experimental studies of the network scenarios considered in the paper. Eigenvectors and eigenflows in the subspace are jointly used as factors (features) for linear regression to forecast the network link traffic. It is demonstrated that the eigensubspace based auto-regressive order two, AR (2), predictor is superior to the time-domain based predictor to forecast the link level traffic of a network.