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Low-rank representation for internet traffic reconstruction using compressive sampling
Irawati I.D.a,b, Edward I.J.M.b, Suksmono A.B.b
a Telkom Applied Science School, Telkom University, Bandung, Indonesia
b School of Electrical and Informatics, Institut Teknologi Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2018 Universiti Teknikal Malaysia Melaka. All rights reserved.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.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Compressive Sampling,Internet Traffic Matrix,Low-Rank,SVD[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]