Link-level interpretation of eigenanalysis for network traffic flows

Document Type

Conference Proceeding

Publication Date

5-10-2017

Abstract

This paper presents a novel approach to interpret eigenanalysis of network statistics at the link level in order to identify traffic flows efficiently. It jointly uses and interprets eigencoefficients (frequency) and components of eigenvectors (time) to quantify their importance on each sample (each component of link traffic vector) in eigensubspace representation. We apply the proposed method to analyze the traffic data obtained from Internet2 network. Its merit and superiority over eigenflow based traditional analysis methods are displayed for a few network scenarios with anomalies. It is highlighted that the link-level resolution provided by the method offers advantages also for multi-layer traffic engineering, and it is currently being studied by the authors.

Identifier

85020221756 (Scopus)

ISBN

[9781509047802]

Publication Title

2017 51st Annual Conference on Information Sciences and Systems Ciss 2017

External Full Text Location

https://doi.org/10.1109/CISS.2017.7926117

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