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dc.contributor.authorLakhina, Anukoolen_US
dc.contributor.authorPapagiannaki, Konstantinaen_US
dc.contributor.authorCrovella, Marken_US
dc.contributor.authorDiot, Christopheen_US
dc.contributor.authorKoloczyk, Eric D.en_US
dc.contributor.authorTaft, Ninaen_US
dc.date.accessioned2011-10-20T04:15:34Z
dc.date.available2011-10-20T04:15:34Z
dc.date.issued2003-11-10en_US
dc.identifier.urihttps://hdl.handle.net/2144/1517
dc.description.abstractNetwork traffic arises from the superposition of Origin-Destination (OD) flows. Hence, a thorough understanding of OD flows is essential for modeling network traffic, and for addressing a wide variety of problems including traffic engineering, traffic matrix estimation, capacity planning, forecasting and anomaly detection. However, to date, OD flows have not been closely studied, and there is very little known about their properties. We present the first analysis of complete sets of OD flow timeseries, taken from two different backbone networks (Abilene and Sprint-Europe). Using Principal Component Analysis (PCA), we find that the set of OD flows has small intrinsic dimension. In fact, even in a network with over a hundred OD flows, these flows can be accurately modeled in time using a small number (10 or less) of independent components or dimensions. We also show how to use PCA to systematically decompose the structure of OD flow timeseries into three main constituents: common periodic trends, short-lived bursts, and noise. We provide insight into how the various constituents contribute to the overall structure of OD flows and explore the extent to which this decomposition varies over time.en_US
dc.description.sponsorshipOffice of Naval Research (N000140310043); Sprint Labs; National Science Foundation (ANI-9986397, CCR-0325701)en_US
dc.language.isoen_USen_US
dc.publisherBoston University Computer Science Departmenten_US
dc.relation.ispartofseriesBUCS Technical Reports;BUCS-TR-2003-021en_US
dc.titleStructural Analysis of Network Traffic Flowsen_US
dc.typeTechnical Reporten_US


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