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A Novel Compressed Sensing Technique for Traffic Matrix Estimation of Software Defined Cloud Networks
  • KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

A Novel Compressed Sensing Technique for Traffic Matrix Estimation of Software Defined Cloud Networks

Vol. 12, No. 10, October 30, 2018
10.3837/tiis.2018.10.004, Download Paper (Free):

Abstract

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. Previously, the researchers had relied on the assumption that the measurement (routing) matrix is stationary due to which the schemes are not suitable for modern software defined networks. 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 . Experimental evaluation of proposed technique using real world datasets Abilene and GEANT shows that the technique is practical to be used in modern software defined networks. Further, the performance of the scheme is compared with recent state of the art techniques proposed in research literature.


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Cite this article

[IEEE Style]
S. Qazi, S. M. Atif and M. B. Kadri, "A Novel Compressed Sensing Technique for Traffic Matrix Estimation of Software Defined Cloud Networks," KSII Transactions on Internet and Information Systems, vol. 12, no. 10, pp. 4678-4702, 2018. DOI: 10.3837/tiis.2018.10.004.

[ACM Style]
Sameer Qazi, Syed Muhammad Atif, and Muhammad Bilal Kadri. 2018. A Novel Compressed Sensing Technique for Traffic Matrix Estimation of Software Defined Cloud Networks. KSII Transactions on Internet and Information Systems, 12, 10, (2018), 4678-4702. DOI: 10.3837/tiis.2018.10.004.