Low-Rank Representation for Internet Traffic Reconstruction Using Compressive Sampling

Authors

  • Indrarini Dyah Irawati Telkom Applied Science School, Telkom University, Bandung, Indonesia. School of Electrical and Informatics, Institut Teknologi Bandung, Indonesia.
  • Ian Joseph Matheus Edward School of Electrical and Informatics, Institut Teknologi Bandung, Indonesia.
  • Andriyan Bayu Suksmono School of Electrical and Informatics, Institut Teknologi Bandung, Indonesia.

Keywords:

Compressive Sampling, Low-Rank, Internet Traffic Matrix, SVD,

Abstract

We study compressive sampling for internet traffic reconstruction. Compressive Sampling (CS) requires that the traffic satisfies the low-rank feature. Low-rank states that traffic matrix can be represented in the right domain which the entire necessary information is concentrated in a low number of coefficients. In this paper, we compared three low-rank representation, which are Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and Singular Value Decomposition Mean (SVDM). This low-rank representation is applied to four CS reconstruction algorithms, namely: Sparsity Regularized Singular Value Decomposition (SRSVD), Singular Value Decomposition L1 (SVDL1), Iteratively Reweighted Least Square (IRLS), Orthogonal Matching Pursuit (OMP), and Interpolation. The SVD outperforms the others low-rank representation techniques when used together with SRSVD, SVDL1, IRLS, and Interpolation. The SVDM gives the best NMAE when applied to the OMP. The computational times is linear with the number of the rank matrix. For all reconstruction algorithms, SVDM takes the least computational times.

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Published

2018-01-29

How to Cite

Irawati, I. D., Edward, I. J. M., & Suksmono, A. B. (2018). Low-Rank Representation for Internet Traffic Reconstruction Using Compressive Sampling. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-4), 147–152. Retrieved from https://jtec.utem.edu.my/jtec/article/view/3608

Issue

Vol. 10 No. 1-4: Breakthrough To Excellence in Communication and Computer Engineering I

Section

Articles

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