Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Novel Compressed Sensing Technique for Traffic Matrix Estimation of
  Software Defined Cloud Networks   Traffic Matrix estimation has always caught attention from researchers for
better network management and future planning. With the advent of high traffic
loads due to Cloud Computing platforms and Software Defined Networking based
tunable routing and traffic management algorithms on the Internet, it is more
necessary as ever to be able to predict current and future traffic volumes on
the network. For large networks such origin-destination traffic prediction
problem takes the form of a large under-constrained and under-determined system
of equations with a dynamic measurement matrix. In this work, we present our
Compressed Sensing with Dynamic Model Estimation (CS-DME) architecture suitable
for modern software defined networks. Our main contributions are: (1) we
formulate an approach in which measurement matrix in the compressed sensing
scheme can be accurately and dynamically estimated through a reformulation of
the problem based on traffic demands. (2) We show that the problem formulation
using a dynamic measurement matrix based on instantaneous traffic demands may
be used instead of a stationary binary routing matrix which is more suitable to
modern Software Defined Networks that are constantly evolving in terms of
routing by inspection of its Eigen Spectrum using two real world datasets. (3)
We also show that linking this compressed measurement matrix dynamically with
the measured parameters can lead to acceptable estimation of Origin Destination
(OD) Traffic flows with marginally poor results with other state-of-art schemes
relying on fixed measurement matrices. (4) Furthermore, using this compressed
reformulated problem, a new strategy for selection of vantage points for most
efficient traffic matrix estimation is also presented through a secondary
compression technique based on subset of link measurements.

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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: atom-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci
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The categories of this paper are: math
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The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: patt-sol
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The categories of this paper are: alg-geom
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The categories of this paper are: econ
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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: adap-org