[vc_empty_space][vc_empty_space]
Internet traffic matrix estimation based on compressive sampling
Irawati I.D.a,b, Suksmono A.B.a, Edward I.J.M.a
a School of Electrical and Informatics, Institut Teknologi Bandung, Indonesia
b Telkom Applied Science School, Telkom University, 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]© 2017 American Scientific Publishers All rights reserved.A method for estimating traffic matrix is needed for network operator. Traffic matrix is an essential parameter for network maintenance, network planning, and network monitoring. However, it is very difficult to measure it directly. Hence, traffic matrix is inferred from direct link measurement by estimation. There are various techniques for traffic matrix estimation. This paper proposed SVD-l1 (Singular Value Decomposition-l1) for traffic matrix estimation based on compressive sampling. The proposed method is compared with IRLS (Iteratively Reweighted Least Square), SRSVD (Sparsity Regularized Singular Value Decomposition), OMP (Orthogonal Matching Pursuit), and interpolation techniques. The result showed that the NMAE (Normalized Mean Absolute Error) performance parameter of SVD-l1 as good as SRSVD and better than the other algorithms. The computational time of SVD-l1 is the longest compared to others.[/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,Estimation,Internet traffic matrix,Interpolation,Low-rank[/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]https://doi.org/10.1166/asl.2017.8283[/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]