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- W2800941002 abstract "An end-to-end network traffic matrix is significantly helpful for network management and for Software Defined Networks (SDN). However, the end-to-end network traffic matrix's inferences and estimations are a challenging problem. Moreover, attaining the traffic matrix in high-speed networks for SDN is a prohibitive challenge. This paper investigates how to estimate and recover the end-to-end network traffic matrix in fine time granularity from the sampled traffic traces, which is a hard inverse problem. Different from previous methods, the fractal interpolation is used to reconstruct the finer-granularity network traffic. Then, the cubic spline interpolation method is used to obtain the smooth reconstruction values. To attain an accurate the end-to-end network traffic in fine time granularity, we perform a weighted-geometric-average process for two interpolation results that are obtained. The simulation results show that our approaches are feasible and effective." @default.
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- W2800941002 date "2018-05-02" @default.
- W2800941002 modified "2023-10-01" @default.
- W2800941002 title "Fine-granularity inference and estimations to network traffic for SDN" @default.
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- W2800941002 doi "https://doi.org/10.1371/journal.pone.0194302" @default.
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