IMC '05, 2005 Internet Measurement Conference Abstract
Pp. 253266 of the Proceedings
Sparse Approximations for High Fidelity Compression of Network Traffic Data
William Aiello, University of British Columbia; Anna Gilbert, University of Michigan; Brian Rexroad, AT&T Labs; Vyas Sekar, Carnegie Mellon University
An important component of traffic analysis and network monitoring is
the ability to correlate events across multiple data streams, from
different sources and from different time periods. Storing such a large
amount of data for visualizing traffic trends and for building
prediction models of ``normal'' network traffic represents a great
challenge because the data sets are enormous. In this paper we present
the application and analysis of signal processing techniques for
effective practical compression of network traffic data. We propose to
use a sparse approximation of the network traffic data over a
rich collection of natural building blocks, with several natural
dictionaries drawn from the networking community's experience with
traffic data. We observe that with such natural dictionaries, high
fidelity compression of the original traffic data can be achieved such
that even with a compression ratio of around 1:6, the compression error,
in terms of the energy of the original signal lost, is less than 1%. We
also observe that the sparse representations are stable over time, and
that the stable components correspond to well-defined periodicities in
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